pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Tue Jan 27 09:00:00 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-01-27_100000.log
Parsed 2,655,179 log entries in 58s
Log start from 2026-01-27 10:00:00 to 2026-01-27 10:59:55

Global Stats

282 Number of unique normalized queries
272,740 Number of queries
1h35m42s Total query duration
2026-01-27 10:00:00 First query
2026-01-27 10:59:55 Last query
6,416 queries/s at 2026-01-27 10:15:04 Query peak

1h35m42s Total query duration
11s933ms Prepare/parse total duration
1m3s Bind total duration
1h34m27s Execute total duration

36 Number of events
2 Number of unique normalized events
34 Max number of times the same event was reported
0 Number of cancellation

39 Total number of automatic vacuums
55 Total number of automatic analyzes

630 Number temporary file
171.23 MiB Max size of temporary file
8.62 MiB Average size of temporary file

4,760 Total number of sessions
13 sessions at 2026-01-27 10:53:48 Session peak
4d2h2m49s Total duration of sessions
1m14s Average duration of sessions
57 Average queries per session
1s206ms Average queries duration per session
1m12s Average idle time per session

4,766 Total number of connections
36 connections/s at 2026-01-27 10:45:51 Connection peak
3 Total number of databases

SQL Traffic

Key values

6,416 queries/s Query Peak
2026-01-27 10:15:04 Date

SELECT Traffic

Key values

3,119 queries/s Query Peak
2026-01-27 10:15:04 Date

INSERT/UPDATE/DELETE Traffic

Key values

188 queries/s Query Peak
2026-01-27 10:30:52 Date

Queries duration

Key values

1h35m42s Total query duration

Prepared queries ratio

Key values

0.00 Ratio of bind vs prepare
0.00 % Ratio between prepared and "usual" statements

General Activity

Day	Hour	Count	Min duration	Max duration	Avg duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Jan 27	10	272,740	0ms	38s193ms	20ms	3m36s	4m6s	4m57s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Jan 27	10	82,804	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Jan 27	10	31,218	3,862	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Jan 27	10	31,269	103,754	3.32	24.36%

Day	Hour	Count	Average / Second
Jan 27	10	4,766	1.32/s

Day	Hour	Count	Average Duration	Average idle time
Jan 27	10	4,760	1m14s	1m12s

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Established Connections

Key values

36 connections Connection Peak
2026-01-27 10:45:51 Date

Connections per database

Key values

acaweb_fx Main Database
4,766 connections Total

Chart
Table

Database	User	Count
acaweb_fx		4,657
	postgres	4,270
	psql	387
socialmedia		109
	postgres	109

Connections per user

Key values

postgres Main User
4,766 connections Total

Chart
Table

User	Count
postgres	4,379
psql	387

Connections per host

Key values

192.168.1.15 Main host with 1502 connections
4,766 Total connections

Chart
Table

Host	Count
127.0.0.1	112
192.168.0.114	13
192.168.0.171	4
192.168.0.216	101
192.168.0.74	507
192.168.1.127	6
192.168.1.145	168
192.168.1.15	1,502
192.168.1.154	3
192.168.1.20	188
192.168.1.231	20
192.168.1.239	3
192.168.1.90	122
192.168.2.126	68
192.168.2.182	12
192.168.2.82	48
192.168.3.199	36
192.168.4.142	1,126
192.168.4.150	10
192.168.4.171	1
192.168.4.238	16
192.168.4.251	4
192.168.4.33	91
192.168.4.98	330
[local]	275

Simultaneous sessions

Key values

13 sessions Session Peak
2026-01-27 10:53:48 Date

Histogram of session times

Key values

3,968 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	3,968	83.36%
500ms-1s	160	3.36%
1s-30s	384	8.07%
30s-1m	81	1.70%
1m-10m	81	1.70%
10m-30m	68	1.43%
30m-1h	4	0.08%
1h-8h	12	0.25%
> 8h	2	0.04%

Sessions per database

Key values

acaweb_fx Main Database
4,760 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	4,651	4d46m36s	1m14s
socialmedia	109	1h16m13s	41s954ms

Sessions per user

Key values

postgres Main User
4,760 sessions Total

Chart
Table

User	Count	Total Duration	Average Duration
postgres	1	0ms	0ms

Sessions per host

Key values

192.168.1.15 Main Host
4,760 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
127.0.0.1	112	5s163ms	46ms
192.168.0.114	13	1h17m36s	5m58s
192.168.0.171	1	5ms	5ms
192.168.0.216	101	1m2s	617ms
192.168.0.74	509	1d20h9m59s	5m12s
192.168.1.127	6	584ms	97ms
192.168.1.145	168	3h37m26s	1m17s
192.168.1.15	1,499	4h9m4s	9s969ms
192.168.1.154	1	4ms	4ms
192.168.1.20	188	14h30m10s	4m37s
192.168.1.231	20	9h53m17s	29m39s
192.168.1.239	3	34ms	11ms
192.168.1.90	122	38s65ms	312ms
192.168.2.126	68	7s904ms	116ms
192.168.2.182	12	1s383ms	115ms
192.168.2.82	48	12s147ms	253ms
192.168.3.199	36	1s514ms	42ms
192.168.4.142	1,126	8m31s	454ms
192.168.4.150	10	20h7m16s	2h43s
192.168.4.171	1	205ms	205ms
192.168.4.238	16	20s557ms	1s284ms
192.168.4.251	4	18s852ms	4s713ms
192.168.4.33	91	3m35s	2s371ms
192.168.4.98	330	15s147ms	45ms
[local]	275	2m48s	612ms

Sessions per application

Key values

unknown Main Host
4,760 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	4,760	4d2h2m49s	1m14s

Checkpoints Buffers

Key values

15,309 buffers Checkpoint Peak
2026-01-27 10:07:52 Date
209.933 seconds Highest write time
0.012 seconds Sync time

Checkpoints Wal files

Key values

7 files Wal files usage Peak
2026-01-27 10:22:52 Date

Checkpoints distance

Key values

240.37 Mo Distance Peak
2026-01-27 10:22:52 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Jan 27	10	59,912	2,009.739s	0.067s	2,010.115s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Jan 27	10	0	0	31	1,832	0.007s	0s

Day	Hour	Count	Avg time (sec)
Jan 27	10	0	0s

Day	Hour	Mean distance	Mean estimate
Jan 27	10	41,993.83 kB	100,433.25 kB

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Checkpoint causes

Key values

time (12) Main checkpoint cause
12 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	12
Total	12

Size of temporary files

Key values

184.87 MiB Temp Files size Peak
2026-01-27 10:30:08 Date

Number of temporary files

Key values

30 per second Temp Files Peak
2026-01-27 10:02:08 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Jan 27	10	630	5.30 GiB	8.62 MiB

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Queries generating the most temporary files (N)

Rank	Count	Total size	Min size	Max size	Avg size	Query
1	30	1.65 GiB	3.32 MiB	171.23 MiB	56.46 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = ? ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = ? ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = ?) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, ?::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> ? ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = ?) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = ? where (ok.r is null or ok.r = ?) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = ?) and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > ? ? and last.eventtimestamp > current_timestamp - interval ? and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval ?) and last.eventtimestamp > current_timestamp - interval ? and broker.r = ?; with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; Date: 2026-01-27 10:00:06 Duration: 0ms
2	20	76.42 MiB	3.81 MiB	3.83 MiB	3.82 MiB	select resultuid from relevance_fibonacci_results order by resultuid desc limit ?), fr as ( select a., rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp)))), results as ( select distinct on (symbolid) from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), fr AS ( SELECT a., rr.age, rr.relevant from fibonacci_results a LEFT OUTER JOIN relevance_fibonacci_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) END), all_results AS ( SELECT fr.resultuid AS resultuid, fr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, fr.pattern AS pattern_name, fr.timed AS timed, fr.patternendtime AS identified, dtt.timezone AS timezone, fr.patternlengthbars AS length, g.basegroupname, newLevels.filtered, CASE WHEN fr.age IS NOT NULL THEN fr.age WHEN fr.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN symbolgroup sg on fr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN downloadersymbolsettings dss ON fr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN rar_max rm ON 1 = 1 LEFT JOIN LATERAL calc_fib_signal_filter (fr.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4, $5, $6, $7, $8, $9, $10)) AND ($11 = 0 OR s.exchange in ($12)) AND ($13 = 0 OR coalesce(bim.code, s.symbol) in ($14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226)) AND ($227 = 0 OR fr.pattern in ($228)) AND ($229 = 0 OR fr.patternlengthbars <= $230) AND ($231 = 0 OR ($232 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($233 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $234 OR relevant = 1) AND ($235 = 0 OR age <= $236) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-01-27 10:02:35 Duration: 0ms
3	17	126.57 MiB	7.42 MiB	7.48 MiB	7.45 MiB	jr.resultuid as resultuid, jr.direction as direction, jr.patternendtime as identified, jr.patternlengthbars as length, jr.patternstarttime as patternstarttime, case when jr.trendchangeid = ? then ? else ? end as trendchange, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, jp.patternname as pattern_name, dtt.timezone as timezone, ? as age, cps.pip, g.basegroupname from japsticks_results jr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = jr.symbolid inner join relevance_japsticks_results rar on rar.resultuid = jr.resultuid inner join symbols s on jr.symbolid = s.symbolid and s.nonliquid = ? inner join japsticks_patterns jp on jr.patternid = jp.id inner join downloadersymbolsettings dss on jr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where jr.gmttimefound > now() - interval ? and s.deleted = ? and (jr.simulation = ? or jr.simulation is null) and (rar.relevant = ?) --and (semicolon_age = ? or rar.age <= semicolon_age) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or jp.patternname in (...)) and (? = ? or jr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results order by symbolid, resultuid ) select * from results order by identified desc, length desc ; jr.resultuid AS resultuid, jr.direction AS direction, jr.patternendtime AS identified, jr.patternlengthbars AS length, jr.patternstarttime AS patternstarttime, case when jr.trendchangeid = 1 then 'Continuation' else 'Reversal' end AS trendchange, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, jp.patternname AS pattern_name, dtt.timezone AS timezone, 0 AS age, cps.pip, g.basegroupname FROM japsticks_results jr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = jr.symbolid INNER JOIN relevance_japsticks_results rar ON rar.resultuid = jr.resultuid INNER JOIN symbols s ON jr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN japsticks_patterns jp ON jr.patternid = jp.id INNER JOIN downloadersymbolsettings dss ON jr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on s.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE jr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (jr.simulation = 0 OR jr.simulation IS NULL) AND (rar.relevant = 1) --AND (semicolon_age = 0 OR rar.age <= semicolon_age) AND ($2 = 0 OR s.timegranularity in ($3)) AND ($4 = 0 OR s.exchange in ($5)) AND ($6 = 0 OR coalesce(bim.code, s.symbol) in ($7)) AND ($8 = 0 OR jp.patternname in ($9)) AND ($10 = 0 OR jr.patternlengthbars <= $11)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-01-27 10:00:20 Duration: 0ms
4	16	737.38 MiB	46.09 MiB	46.09 MiB	46.09 MiB	update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type = ?) sub where result_uid = sub.resultuid; UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type = 'cp') sub WHERE result_uid = sub.resultuid; Date: 2026-01-27 10:01:13 Duration: 0ms
5	16	1.22 GiB	78.21 MiB	78.21 MiB	78.21 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ?) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then ? else ? end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then ? else ? end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike ? inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike ?) sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike ?; update solr_relevance_old set newrelevant = ? where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null); update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type in (...)) sub where result_uid = sub.resultuid; with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; Date: 2026-01-27 10:01:17 Duration: 0ms
6	8	1019.76 MiB	127.43 MiB	127.51 MiB	127.47 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-01-27 10:02:13 Duration: 0ms
7	4	335.11 MiB	83.70 MiB	83.84 MiB	83.78 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-01-27 10:02:04 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	171.23 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:00:06 ]
2	127.51 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:32:15 ]
3	127.51 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:47:12 ]
4	127.49 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:50:33 ]
5	127.48 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:35:32 ]
6	127.45 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:02:13 ]
7	127.45 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:17:13 ]
8	127.44 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:20:33 ]
9	127.43 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-27 10:05:33 ]
10	116.90 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:10:04 ]
11	111.00 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:40:04 ]
12	103.16 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:20:05 ]
13	90.98 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:40:05 ]
14	88.47 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:50:04 ]
15	87.28 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:10:07 ]
16	86.39 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:50:07 ]
17	84.31 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-27 10:30:05 ]
18	83.84 MiB	select updateageforrelevantresults (); [ Date: 2026-01-27 10:02:04 ]
19	83.83 MiB	select updateageforrelevantresults (); [ Date: 2026-01-27 10:32:06 ]
20	83.74 MiB	select updateageforrelevantresults (); [ Date: 2026-01-27 10:47:04 ]

Vacuums / Analyzes Distribution

Key values

0 sec Highest CPU-cost vacuum
Table
Database
Date
0 sec Highest CPU-cost analyze
Table
Database
Date

Analyzes per table

Key values

public.solr_relevance_old (16) Main table analyzed (database acaweb_fx)
55 analyzes Total

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	6
acaweb_fx.pg_catalog.pg_type	4
acaweb_fx.public.datafeeds_latestrun	4
acaweb_fx.public.relevance_keylevels_results	4
acaweb_fx.pg_catalog.pg_class	4
acaweb_fx.public.relevance_autochartist_results	4
acaweb_fx.public.relevance_fibonacci_results	4
acaweb_fx.pg_catalog.pg_index	2
acaweb_fx.public.latest_t15_candle_view	2
acaweb_fx.public.autochartist_symbolupdates	1
acaweb_fx.public.solr_imports	1
acaweb_fx.public.symbollatestupdatetime	1
acaweb_fx.pg_catalog.pg_depend	1
acaweb_fx.public.latest_candle_datetime_per_receng	1
Total	55

Vacuums per table

Key values

public.solr_relevance_old (16) Main table vacuumed on database acaweb_fx
39 vacuums Total

Chart
Table

		Index	Buffer usage			Skipped		WAL usage
Table	Vacuums	scans	hits	misses	dirtied	pins	frozen	records	full page	bytes
acaweb_fx.public.solr_relevance_old	16	16	12,860	0	49	0	0	9,136	16	1,637,421
acaweb_fx.public.datafeeds_latestrun	4	0	469	0	1	0	0	60	4	37,670
acaweb_fx.pg_toast.pg_toast_2619	2	2	327	0	61	0	0	207	57	222,403
acaweb_fx.pg_catalog.pg_attribute	2	2	1,626	0	368	0	134	745	287	1,689,350
acaweb_fx.pg_catalog.pg_statistic	2	2	1,970	0	333	0	1,176	900	301	1,218,080
acaweb_fx.public.relevance_keylevels_results	2	2	7,708	0	237	2	152	2,373	222	710,021
acaweb_fx.public.relevance_autochartist_results	2	2	7,010	0	150	2	485	1,542	135	358,711
acaweb_fx.pg_catalog.pg_class	2	2	933	0	136	0	0	299	132	627,039
acaweb_fx.public.relevance_fibonacci_results	2	2	2,552	0	52	2	98	467	36	142,695
acaweb_fx.pg_catalog.pg_type	1	1	133	0	26	0	0	51	20	125,562
acaweb_fx.public.fibonacci_results	1	1	107,252	0	20,611	0	164	30,230	18,720	63,355,107
acaweb_fx.public.autochartist_symbolupdates	1	1	22,583	0	3,886	3	38,121	6,974	3,828	1,673,078
acaweb_fx.public.symbollatestupdatetime	1	1	1,607	0	268	0	642	1,080	264	714,064
acaweb_fx.public.latest_t15_candle_view	1	1	66	0	1	0	0	6	1	9,061
Total	39	35	167,096	152,239	26,179	9	40,972	54,070	24,023	72,520,262

Tuples removed per table

Key values

public.solr_relevance_old (50685) Main table with removed tuples on database acaweb_fx
87732 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	16	16	50,685	89,528	0	0	3,153
acaweb_fx.public.symbollatestupdatetime	1	1	18,803	92,167	149	0	1,714
acaweb_fx.public.autochartist_symbolupdates	1	1	5,473	48,573	39	0	40,691
acaweb_fx.public.fibonacci_results	1	1	3,999	106,033	0	0	9,004
acaweb_fx.pg_catalog.pg_attribute	2	2	2,762	21,737	0	40	480
acaweb_fx.public.relevance_keylevels_results	2	2	2,316	25,017	0	0	558
acaweb_fx.pg_catalog.pg_statistic	2	2	1,214	7,473	0	0	2,388
acaweb_fx.public.relevance_autochartist_results	2	2	1,167	16,519	0	0	760
acaweb_fx.public.relevance_fibonacci_results	2	2	418	2,799	0	0	204
acaweb_fx.pg_catalog.pg_class	2	2	295	3,298	0	0	300
acaweb_fx.public.datafeeds_latestrun	4	0	233	56	0	0	64
acaweb_fx.pg_toast.pg_toast_2619	2	2	154	338	2	0	102
acaweb_fx.pg_catalog.pg_type	1	1	153	1,446	0	0	38
acaweb_fx.public.latest_t15_candle_view	1	1	60	14	0	0	1
Total	39	35	87,732	414,998	190	40	59,457

Pages removed per table

Key values

pg_catalog.pg_attribute (40) Main table with removed pages on database acaweb_fx
40 pages Total removed

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_attribute	2	2	2762	40
acaweb_fx.pg_toast.pg_toast_2619	2	2	154	0
acaweb_fx.pg_catalog.pg_type	1	1	153	0
acaweb_fx.public.fibonacci_results	1	1	3999	0
acaweb_fx.public.autochartist_symbolupdates	1	1	5473	0
acaweb_fx.public.datafeeds_latestrun	4	0	233	0
acaweb_fx.pg_catalog.pg_statistic	2	2	1214	0
acaweb_fx.public.symbollatestupdatetime	1	1	18803	0
acaweb_fx.public.latest_t15_candle_view	1	1	60	0
acaweb_fx.public.relevance_keylevels_results	2	2	2316	0
acaweb_fx.public.solr_relevance_old	16	16	50685	0
acaweb_fx.public.relevance_autochartist_results	2	2	1167	0
acaweb_fx.pg_catalog.pg_class	2	2	295	0
acaweb_fx.public.relevance_fibonacci_results	2	2	418	0
Total	39	35	87,732	40

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Jan 27	10	39	55

↑ Back to the top of the Autovacuum Activity table

Locks by types

Key values

unknown Main Lock Type
0 locks Total

Chart
Table

Type of locks

NO DATASET

Type	Object	Count	Total Duration	Average Duration (s)
NO DATASET

Most frequent waiting queries (N)

Rank	Count	Total time	Min time	Max time	Avg duration	Query
NO DATASET

Queries that waited the most

Rank	Wait time	Query
NO DATASET

Queries by type

Key values

82,804 Total read queries
45,564 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	82,804	30.36%
INSERT	31,218	11.45%
UPDATE	3,862	1.42%
DELETE	16	0.01%
COPY FROM	96	0.04%
COPY TO	26	0.01%
CTE	9,462	3.47%
DDL	16	0.01%
TCL	868	0.32%
CURSOR	0	0.00%
OTHERS	144,372	52.93%

Queries by database

Key values

unknown Main database
271,711 Requests
1h34m27s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	920	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	204	0ms
	select	103	0ms
	tcl	333	0ms
	update	38	0ms
socialmedia	Total	109	0ms
	others	6	0ms
	select	91	0ms
	tcl	12	0ms
unknown	Total	271,711	1h34m27s
	copy from	16	0ms
	cte	9,358	0ms
	insert	31,218	0ms
	others	7,808	0ms
	select	82,610	0ms
	tcl	523	0ms
	update	3,824	0ms

Queries by user

Key values

unknown Main user
271,711 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	1,029	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	210	0ms
	select	194	0ms
	tcl	345	0ms
	update	38	0ms
unknown	Total	271,711	1h34m27s
	copy from	16	0ms
	cte	9,358	0ms
	insert	31,218	0ms
	others	7,808	0ms
	select	82,610	0ms
	tcl	523	0ms
	update	3,824	0ms

Duration by user

Key values

1h34m27s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	1,029	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	210	0ms
	select	194	0ms
	tcl	345	0ms
	update	38	0ms
unknown	Total	271,711	1h34m27s
	copy from	16	0ms
	cte	9,358	0ms
	insert	31,218	0ms
	others	7,808	0ms
	select	82,610	0ms
	tcl	523	0ms
	update	3,824	0ms

Queries by host

Key values

unknown Main host
272,740 Requests
1h34m27s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	272,740	1h34m27s
	copy from	96	0ms
	copy to	26	0ms
	cte	9,462	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	31,218	0ms
	others	8,018	0ms
	select	82,804	0ms
	tcl	868	0ms
	update	3,862	0ms

Queries by application

Key values

unknown Main application
272,353 Requests
1h34m27s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
psql	Total	387	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	4	0ms
	select	103	0ms
	update	38	0ms
unknown	Total	272,353	1h34m27s
	copy from	16	0ms
	cte	9,358	0ms
	insert	31,218	0ms
	others	8,014	0ms
	select	82,701	0ms
	tcl	868	0ms
	update	3,824	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2026-01-27 10:21:55 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

91,262 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	91,262	66.93%
1-5ms	40,364	29.60%
5-10ms	2,066	1.52%
10-25ms	696	0.51%
25-50ms	330	0.24%
50-100ms	178	0.13%
100-500ms	397	0.29%
500-1000ms	153	0.11%
1000-10000ms	737	0.54%
> 10000ms	171	0.13%

Slowest individual queries

Rank	Duration	Query
NO DATASET

Time consuming queries

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

0ms

1

Details

0ms

select s.statsid, s.description, s.groupingname, cast(ss.percentage as integer) as cppercentage, cast(hss.percentage as integer) as klpercentage, cast(hass.percentage as integer) as aklpercentage, case when b.name ilike ? then ? else ? end as showaclogo from broker b inner join stats s on b.brokerid = s.brokerid left outer join stats_summary ss on ss.statsid = s.statsid left outer join stats_hrs_summary hss on hss.statsid = s.statsid left outer join stats_hrsapproaches_summary hass on hass.statsid = s.statsid where s.brokerid = ? and ss.total > ? and ss.category ilike ? and hss.category ilike ? and hass.category ilike ? group by s.statsid, s.description, s.brokerid, s.latestupdate, s.groupingname, s.calcfrom, s.calcto, ss.statsid, ss.percentage, hss.percentage, hass.percentage, b.name;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

2

0ms

38

Details

0ms

select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Jan 27	10	38	0ms	0ms

3

0ms

15

Details

0ms

with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Jan 27	10	15	0ms	0ms

4

0ms

2,155

Details

0ms

insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Jan 27	10	2,155	0ms	0ms

5

0ms

48

Details

0ms

select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Jan 27	10	48	0ms	0ms

6

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Jan 27	10	4	0ms	0ms

7

0ms

6

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Jan 27	10	6	0ms	0ms

8

0ms

6

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Jan 27	10	6	0ms	0ms

9

0ms

18

Details

0ms

select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Jan 27	10	18	0ms	0ms

10

0ms

434

Details

0ms

commit;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Jan 27	10	434	0ms	0ms

11

0ms

311

Details

0ms

with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ), kr as ( select a.*, rr.age, rr.relevant from keylevels_results a left outer join relevance_keylevels_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_keylevels_results) end ), all_results as ( select kr.resultuid as resultuid, kr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, p.patternname as pattern_name, kr.breakout as breakout, kr.atbaridentified as identified, dtt.timezone as timezone, kr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when kr.age is not null then kr.age when kr.resultuid <= rm.resultuid then ? else ? end as age, case when kr.relevant is not null then kr.relevant when kr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from kr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = kr.symbolid inner join symbols s on bsl.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join hrspatterns p on kr.patternid = p.patternid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left outer join autochartist_symbolupdates au on dss.symbolid = au.symbolid left outer join relevance_keylevels_results rar on rar.resultuid = kr.resultuid left join lateral calc_kl_signal_filter (kr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where kr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (kr.simulation = ? or kr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or p.patternname in (...)) and (? = ? or kr.patternclassid in (...)) and (? = ? or kr.patternlengthbars <= ?) and kr.patternstarttime::timestamp without time zone >= coalesce(au.earliestpricedatetime, ?::timestamp without time zone) -- to make sure patternstarttime is in our t-tables ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc limit ?;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Jan 27	10	311	0ms	0ms

12

0ms

240

Details

0ms

select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Jan 27	10	240	0ms	0ms

13

0ms

240

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Jan 27	10	240	0ms	0ms

14

0ms

5

Details

0ms

select groupid, exchange, groupname, symbol, longname from prfsymboltree where brokerid = ? order by groupname, symbol;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Jan 27	10	5	0ms	0ms

15

0ms

4

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Jan 27	10	4	0ms	0ms

16

0ms

11

Details

0ms

select updatedatafeedslatestrun (?);

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Jan 27	10	11	0ms	0ms

17

0ms

1

Details

0ms

update executions set isrunning = false, has_results=true, response=?'s products and services include credit card, charge card, banking, and other payment and financing products; network services; expense management products and services; and travel and lifestyle services. It also provides merchant acquisition and processing, servicing and settlement, point-of-sale marketing, and information products and services for merchants; and fraud prevention services, as well as the design and operation of customer loyalty programs. In addition, the company leases and operates lounges at airports. Further, it designs and develops a software to manage company expenses. The company sells its products and services to consumers, small businesses, mid-sized companies, and large corporations through mobile and online applications, affiliate marketing, customer referral programs, third-party service providers and business partners, direct mail, telephone, in-house sales teams, telephone, and direct response advertising. American Express Company was founded in ? and is based in New York, New York.", "Address": "? Vesey Street, New York, NY, United States, ?", "Phone": "(?) ?-2000", "WebURL": "https://www.americanexpress.com", "LogoURL": "https://eodhistoricaldata.com/img/logos/US/axp.png", "FullTimeEmployees": ?, "UpdatedAt": "?-01-27", "MarketCapitalization": ?, "MarketCapitalizationMln": ?.?, "EBITDA": null, "PERatio": ?.?, "PEGRatio": ?.?, "WallStreetTargetPrice": ?.?, "BookValue": ?.?, "DividendShare": ?.?, "DividendYield": ?.?, "EarningsShare": ?.?, "EPSEstimateCurrentYear": ?.?, "EPSEstimateNextYear": ?.?, "EPSEstimateNextQuarter": ?.?, "EPSEstimateCurrentQuarter": ?.?, "MostRecentQuarter": "?-09-30", "ProfitMargin": ?.?, "OperatingMarginTTM": ?.?, "ReturnOnAssetsTTM": ?.?, "ReturnOnEquityTTM": ?.?, "RevenueTTM": ?, "RevenuePerShareTTM": ?.?, "QuarterlyRevenueGrowthYOY": ?.?, "GrossProfitTTM": ?, "DilutedEpsTTM": ?.?, "QuarterlyEarningsGrowthYOY": ?.? }, "?": { "earningsimpactid": ?, "sample_size": ?, "impactcompany": "PYPL", "impactcompany_name": "PayPal Holdings Inc", "impactcompany_sector": "Financial Services", "impactcompany_industry": "Credit Services", "opportunities": [ { "forecastperiod_trading_days": ?, "id": "delta_gt_down", "probability": "?%", "delta_gt": ?, "delta_gt_down": ?, "trend_direction": ?, "delta_sign": ">", "mean_mov": "?.?", "mean_mov_percent": "?%", "icon": "https://acarrows.s3.eu-west-1.amazonaws.com/arrows/down.svg" } ], "Code": "PYPL", "Type": "Common Stock", "Name": "PayPal Holdings Inc", "Exchange": "NASDAQ", "CurrencyCode": "USD", "CurrencyName": "US Dollar", "CurrencySymbol": "$", "CountryName": "USA", "CountryISO": "US", "OpenFigi": "BBG?VNXV?", "ISIN": "US?Y?", "LEI": "?X?GO?EFZ?E?", "PrimaryTicker": "PYPL.US", "CUSIP": "?Y?", "CIK": "?", "EmployerIdNumber": "?-2989869", "FiscalYearEnd": "December", "IPODate": "?-02-01", "InternationalDomestic": "Domestic", "Sector": "Financial Services", "Industry": "Credit Services", "GicSector": "Financials", "GicGroup": "Financial Services", "GicIndustry": "Financial Services", "GicSubIndustry": "Transaction & Payment Processing Services", "HomeCategory": "Domestic", "IsDelisted": false, "Description": "PayPal Holdings, Inc. operates a technology platform that enables digital payments for merchants and consumers worldwide. It operates a two-sided network at scale that connects merchants and consumers that enables its customers to connect, transact, and send and receive payments through online and in person, as well as transfer and withdraw funds using various funding sources, such as bank accounts, PayPal or Venmo account balance, consumer credit products, credit and debit cards, and cryptocurrencies, as well as other stored value products, including gift cards and eligible rewards. The company provides payment solutions under the PayPal, PayPal Credit, Braintree, Venmo, Xoom, Zettle, Hyperwallet, Honey, and Paidy names. The company was founded in ? and is headquartered in San Jose, California.", "Address": "? North First Street, San Jose, CA, United States, ?", "Phone": "? ? ?", "WebURL": "https://www.paypal.com", "LogoURL": "https://eodhistoricaldata.com/img/logos/US/pypl.png", "FullTimeEmployees": ?, "UpdatedAt": "?-01-26", "MarketCapitalization": ?, "MarketCapitalizationMln": ?.?, "EBITDA": ?, "PERatio": ?.?, "PEGRatio": ?.?, "WallStreetTargetPrice": ?.?, "BookValue": ?.?, "DividendShare": ?.?, "DividendYield": ?.?, "EarningsShare": ?.?, "EPSEstimateCurrentYear": ?.?, "EPSEstimateNextYear": ?.?, "EPSEstimateNextQuarter": ?.?, "EPSEstimateCurrentQuarter": ?.?, "MostRecentQuarter": "?-09-30", "ProfitMargin": ?.?, "OperatingMarginTTM": ?.?, "ReturnOnAssetsTTM": ?.?, "ReturnOnEquityTTM": ?.?, "RevenueTTM": ?, "RevenuePerShareTTM": ?.?, "QuarterlyRevenueGrowthYOY": ?.?, "GrossProfitTTM": ?, "DilutedEpsTTM": ?.?, "QuarterlyEarningsGrowthYOY": ?.? }, "?": { "earningsimpactid": "%%OBJNAME?%%earningsimpactid.visible": false, "sample_size": "%%OBJNAME?%%sample_size.visible": false, 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"%%OBJNAME?%%WebURL.visible": false, "LogoURL": "%%OBJNAME?%%LogoURL.visible": false, "FullTimeEmployees": "%%OBJNAME?%%FullTimeEmployees.visible": false, "UpdatedAt": "%%OBJNAME?%%UpdatedAt.visible": false, "MarketCapitalization": "%%OBJNAME?%%MarketCapitalization.visible": false, "MarketCapitalizationMln": "%%OBJNAME?%%MarketCapitalizationMln.visible": false, "EBITDA": "%%OBJNAME?%%EBITDA.visible": false, "PERatio": "%%OBJNAME?%%PERatio.visible": false, "PEGRatio": "%%OBJNAME?%%PEGRatio.visible": false, "WallStreetTargetPrice": "%%OBJNAME?%%WallStreetTargetPrice.visible": false, "BookValue": "%%OBJNAME?%%BookValue.visible": false, "DividendShare": "%%OBJNAME?%%DividendShare.visible": false, "DividendYield": "%%OBJNAME?%%DividendYield.visible": false, "EarningsShare": "%%OBJNAME?%%EarningsShare.visible": false, "EPSEstimateCurrentYear": "%%OBJNAME?%%EPSEstimateCurrentYear.visible": false, "EPSEstimateNextYear": "%%OBJNAME?%%EPSEstimateNextYear.visible": false, 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"earningsimpactid": "%%OBJNAME?%%earningsimpactid.visible": false, "sample_size": "%%OBJNAME?%%sample_size.visible": false, "impactcompany": "%%OBJNAME?%%impactcompany.visible": false, "impactcompany_name": "%%OBJNAME?%%impactcompany_name.visible": false, "impactcompany_sector": "%%OBJNAME?%%impactcompany_sector.visible": false, "impactcompany_industry": "%%OBJNAME?%%impactcompany_industry.visible": false, "opportunities": "%%OBJNAME?%%opportunities.visible": false, "Code": "%%OBJNAME?%%Code.visible": false, "Type": "%%OBJNAME?%%Type.visible": false, "Name": "%%OBJNAME?%%Name.visible": false, "Exchange": "%%OBJNAME?%%Exchange.visible": false, "CurrencyCode": "%%OBJNAME?%%CurrencyCode.visible": false, "CurrencyName": "%%OBJNAME?%%CurrencyName.visible": false, "CurrencySymbol": "%%OBJNAME?%%CurrencySymbol.visible": false, "CountryName": "%%OBJNAME?%%CountryName.visible": false, "CountryISO": "%%OBJNAME?%%CountryISO.visible": false, "OpenFigi": "%%OBJNAME?%%OpenFigi.visible": false, "ISIN": "%%OBJNAME?%%ISIN.visible": false, "LEI": "%%OBJNAME?%%LEI.visible": false, "PrimaryTicker": "%%OBJNAME?%%PrimaryTicker.visible": false, "CUSIP": "%%OBJNAME?%%CUSIP.visible": false, "CIK": "%%OBJNAME?%%CIK.visible": false, "EmployerIdNumber": "%%OBJNAME?%%EmployerIdNumber.visible": false, "FiscalYearEnd": "%%OBJNAME?%%FiscalYearEnd.visible": false, "IPODate": "%%OBJNAME?%%IPODate.visible": false, "InternationalDomestic": "%%OBJNAME?%%InternationalDomestic.visible": false, "Sector": "%%OBJNAME?%%Sector.visible": false, "Industry": "%%OBJNAME?%%Industry.visible": false, "GicSector": "%%OBJNAME?%%GicSector.visible": false, "GicGroup": "%%OBJNAME?%%GicGroup.visible": false, "GicIndustry": "%%OBJNAME?%%GicIndustry.visible": false, "GicSubIndustry": "%%OBJNAME?%%GicSubIndustry.visible": false, "HomeCategory": "%%OBJNAME?%%HomeCategory.visible": false, "IsDelisted": "%%OBJNAME?%%IsDelisted.visible": false, "Description": "%%OBJNAME?%%Description.visible": false, "Address": "%%OBJNAME?%%Address.visible": false, "Phone": "%%OBJNAME?%%Phone.visible": false, "WebURL": "%%OBJNAME?%%WebURL.visible": false, "LogoURL": "%%OBJNAME?%%LogoURL.visible": false, "FullTimeEmployees": "%%OBJNAME?%%FullTimeEmployees.visible": false, "UpdatedAt": "%%OBJNAME?%%UpdatedAt.visible": false, "MarketCapitalization": "%%OBJNAME?%%MarketCapitalization.visible": false, "MarketCapitalizationMln": "%%OBJNAME?%%MarketCapitalizationMln.visible": false, "EBITDA": "%%OBJNAME?%%EBITDA.visible": false, "PERatio": "%%OBJNAME?%%PERatio.visible": false, "PEGRatio": "%%OBJNAME?%%PEGRatio.visible": false, "WallStreetTargetPrice": "%%OBJNAME?%%WallStreetTargetPrice.visible": false, "BookValue": "%%OBJNAME?%%BookValue.visible": false, "DividendShare": "%%OBJNAME?%%DividendShare.visible": false, "DividendYield": "%%OBJNAME?%%DividendYield.visible": false, "EarningsShare": "%%OBJNAME?%%EarningsShare.visible": false, "EPSEstimateCurrentYear": "%%OBJNAME?%%EPSEstimateCurrentYear.visible": false, "EPSEstimateNextYear": "%%OBJNAME?%%EPSEstimateNextYear.visible": false, "EPSEstimateNextQuarter": "%%OBJNAME?%%EPSEstimateNextQuarter.visible": false, "EPSEstimateCurrentQuarter": "%%OBJNAME?%%EPSEstimateCurrentQuarter.visible": false, "MostRecentQuarter": "%%OBJNAME?%%MostRecentQuarter.visible": false, "ProfitMargin": "%%OBJNAME?%%ProfitMargin.visible": false, "OperatingMarginTTM": "%%OBJNAME?%%OperatingMarginTTM.visible": false, "ReturnOnAssetsTTM": "%%OBJNAME?%%ReturnOnAssetsTTM.visible": false, "ReturnOnEquityTTM": "%%OBJNAME?%%ReturnOnEquityTTM.visible": false, "RevenueTTM": "%%OBJNAME?%%RevenueTTM.visible": false, "RevenuePerShareTTM": "%%OBJNAME?%%RevenuePerShareTTM.visible": false, "QuarterlyRevenueGrowthYOY": "%%OBJNAME?%%QuarterlyRevenueGrowthYOY.visible": false, "GrossProfitTTM": "%%OBJNAME?%%GrossProfitTTM.visible": false, "DilutedEpsTTM": "%%OBJNAME?%%DilutedEpsTTM.visible": false, "QuarterlyEarningsGrowthYOY": "%%OBJNAME?%%QuarterlyEarningsGrowthYOY.visible": false, "Shape.visible": false, "bgshape.visible": false }, "?": { "earningsimpactid": "%%OBJNAME?%%earningsimpactid.visible": false, "sample_size": "%%OBJNAME?%%sample_size.visible": false, "impactcompany": "%%OBJNAME?%%impactcompany.visible": false, "impactcompany_name": "%%OBJNAME?%%impactcompany_name.visible": false, "impactcompany_sector": "%%OBJNAME?%%impactcompany_sector.visible": false, "impactcompany_industry": "%%OBJNAME?%%impactcompany_industry.visible": false, "opportunities": "%%OBJNAME?%%opportunities.visible": false, "Code": "%%OBJNAME?%%Code.visible": false, "Type": "%%OBJNAME?%%Type.visible": false, "Name": "%%OBJNAME?%%Name.visible": false, "Exchange": "%%OBJNAME?%%Exchange.visible": false, "CurrencyCode": "%%OBJNAME?%%CurrencyCode.visible": false, "CurrencyName": "%%OBJNAME?%%CurrencyName.visible": false, "CurrencySymbol": "%%OBJNAME?%%CurrencySymbol.visible": false, "CountryName": "%%OBJNAME?%%CountryName.visible": false, "CountryISO": "%%OBJNAME?%%CountryISO.visible": false, "OpenFigi": "%%OBJNAME?%%OpenFigi.visible": false, "ISIN": "%%OBJNAME?%%ISIN.visible": false, "LEI": "%%OBJNAME?%%LEI.visible": false, "PrimaryTicker": "%%OBJNAME?%%PrimaryTicker.visible": false, "CUSIP": "%%OBJNAME?%%CUSIP.visible": false, "CIK": "%%OBJNAME?%%CIK.visible": false, "EmployerIdNumber": "%%OBJNAME?%%EmployerIdNumber.visible": false, "FiscalYearEnd": "%%OBJNAME?%%FiscalYearEnd.visible": false, "IPODate": "%%OBJNAME?%%IPODate.visible": false, "InternationalDomestic": "%%OBJNAME?%%InternationalDomestic.visible": false, "Sector": "%%OBJNAME?%%Sector.visible": false, "Industry": "%%OBJNAME?%%Industry.visible": false, "GicSector": "%%OBJNAME?%%GicSector.visible": false, "GicGroup": "%%OBJNAME?%%GicGroup.visible": false, "GicIndustry": "%%OBJNAME?%%GicIndustry.visible": false, "GicSubIndustry": "%%OBJNAME?%%GicSubIndustry.visible": false, "HomeCategory": "%%OBJNAME?%%HomeCategory.visible": false, "IsDelisted": "%%OBJNAME?%%IsDelisted.visible": false, "Description": "%%OBJNAME?%%Description.visible": false, "Address": "%%OBJNAME?%%Address.visible": false, "Phone": "%%OBJNAME?%%Phone.visible": false, "WebURL": "%%OBJNAME?%%WebURL.visible": false, "LogoURL": "%%OBJNAME?%%LogoURL.visible": false, "FullTimeEmployees": "%%OBJNAME?%%FullTimeEmployees.visible": false, "UpdatedAt": "%%OBJNAME?%%UpdatedAt.visible": false, "MarketCapitalization": "%%OBJNAME?%%MarketCapitalization.visible": false, "MarketCapitalizationMln": "%%OBJNAME?%%MarketCapitalizationMln.visible": false, "EBITDA": "%%OBJNAME?%%EBITDA.visible": false, "PERatio": "%%OBJNAME?%%PERatio.visible": false, "PEGRatio": "%%OBJNAME?%%PEGRatio.visible": false, "WallStreetTargetPrice": "%%OBJNAME?%%WallStreetTargetPrice.visible": false, "BookValue": "%%OBJNAME?%%BookValue.visible": false, "DividendShare": "%%OBJNAME?%%DividendShare.visible": false, "DividendYield": "%%OBJNAME?%%DividendYield.visible": false, "EarningsShare": "%%OBJNAME?%%EarningsShare.visible": false, "EPSEstimateCurrentYear": "%%OBJNAME?%%EPSEstimateCurrentYear.visible": false, "EPSEstimateNextYear": "%%OBJNAME?%%EPSEstimateNextYear.visible": false, "EPSEstimateNextQuarter": "%%OBJNAME?%%EPSEstimateNextQuarter.visible": false, "EPSEstimateCurrentQuarter": "%%OBJNAME?%%EPSEstimateCurrentQuarter.visible": false, "MostRecentQuarter": "%%OBJNAME?%%MostRecentQuarter.visible": false, "ProfitMargin": "%%OBJNAME?%%ProfitMargin.visible": false, "OperatingMarginTTM": "%%OBJNAME?%%OperatingMarginTTM.visible": false, "ReturnOnAssetsTTM": "%%OBJNAME?%%ReturnOnAssetsTTM.visible": false, "ReturnOnEquityTTM": "%%OBJNAME?%%ReturnOnEquityTTM.visible": false, "RevenueTTM": "%%OBJNAME?%%RevenueTTM.visible": false, "RevenuePerShareTTM": "%%OBJNAME?%%RevenuePerShareTTM.visible": false, "QuarterlyRevenueGrowthYOY": "%%OBJNAME?%%QuarterlyRevenueGrowthYOY.visible": false, "GrossProfitTTM": "%%OBJNAME?%%GrossProfitTTM.visible": false, "DilutedEpsTTM": "%%OBJNAME?%%DilutedEpsTTM.visible": false, "QuarterlyEarningsGrowthYOY": "%%OBJNAME?%%QuarterlyEarningsGrowthYOY.visible": false, "Shape.visible": false, "bgshape.visible": false }, "?": { "earningsimpactid": "%%OBJNAME?%%earningsimpactid.visible": false, "sample_size": "%%OBJNAME?%%sample_size.visible": false, "impactcompany": "%%OBJNAME?%%impactcompany.visible": false, "impactcompany_name": "%%OBJNAME?%%impactcompany_name.visible": false, "impactcompany_sector": "%%OBJNAME?%%impactcompany_sector.visible": false, "impactcompany_industry": "%%OBJNAME?%%impactcompany_industry.visible": false, "opportunities": "%%OBJNAME?%%opportunities.visible": false, "Code": "%%OBJNAME?%%Code.visible": false, "Type": "%%OBJNAME?%%Type.visible": false, "Name": "%%OBJNAME?%%Name.visible": false, "Exchange": "%%OBJNAME?%%Exchange.visible": false, "CurrencyCode": "%%OBJNAME?%%CurrencyCode.visible": false, "CurrencyName": "%%OBJNAME?%%CurrencyName.visible": false, "CurrencySymbol": "%%OBJNAME?%%CurrencySymbol.visible": false, "CountryName": "%%OBJNAME?%%CountryName.visible": false, "CountryISO": "%%OBJNAME?%%CountryISO.visible": false, "OpenFigi": "%%OBJNAME?%%OpenFigi.visible": false, "ISIN": "%%OBJNAME?%%ISIN.visible": false, "LEI": "%%OBJNAME?%%LEI.visible": false, "PrimaryTicker": "%%OBJNAME?%%PrimaryTicker.visible": false, "CUSIP": "%%OBJNAME?%%CUSIP.visible": false, "CIK": "%%OBJNAME?%%CIK.visible": false, "EmployerIdNumber": "%%OBJNAME?%%EmployerIdNumber.visible": false, "FiscalYearEnd": "%%OBJNAME?%%FiscalYearEnd.visible": false, "IPODate": "%%OBJNAME?%%IPODate.visible": false, "InternationalDomestic": "%%OBJNAME?%%InternationalDomestic.visible": false, "Sector": "%%OBJNAME?%%Sector.visible": false, "Industry": "%%OBJNAME?%%Industry.visible": false, "GicSector": "%%OBJNAME?%%GicSector.visible": false, "GicGroup": "%%OBJNAME?%%GicGroup.visible": false, "GicIndustry": "%%OBJNAME?%%GicIndustry.visible": false, "GicSubIndustry": "%%OBJNAME?%%GicSubIndustry.visible": false, "HomeCategory": "%%OBJNAME?%%HomeCategory.visible": false, "IsDelisted": "%%OBJNAME?%%IsDelisted.visible": false, "Description": "%%OBJNAME?%%Description.visible": false, "Address": "%%OBJNAME?%%Address.visible": false, "Phone": "%%OBJNAME?%%Phone.visible": false, "WebURL": "%%OBJNAME?%%WebURL.visible": false, "LogoURL": "%%OBJNAME?%%LogoURL.visible": false, "FullTimeEmployees": "%%OBJNAME?%%FullTimeEmployees.visible": false, "UpdatedAt": "%%OBJNAME?%%UpdatedAt.visible": false, "MarketCapitalization": "%%OBJNAME?%%MarketCapitalization.visible": false, "MarketCapitalizationMln": "%%OBJNAME?%%MarketCapitalizationMln.visible": false, "EBITDA": "%%OBJNAME?%%EBITDA.visible": false, "PERatio": "%%OBJNAME?%%PERatio.visible": false, "PEGRatio": "%%OBJNAME?%%PEGRatio.visible": false, "WallStreetTargetPrice": "%%OBJNAME?%%WallStreetTargetPrice.visible": false, "BookValue": "%%OBJNAME?%%BookValue.visible": false, "DividendShare": "%%OBJNAME?%%DividendShare.visible": false, "DividendYield": "%%OBJNAME?%%DividendYield.visible": false, "EarningsShare": "%%OBJNAME?%%EarningsShare.visible": false, "EPSEstimateCurrentYear": "%%OBJNAME?%%EPSEstimateCurrentYear.visible": false, "EPSEstimateNextYear": "%%OBJNAME?%%EPSEstimateNextYear.visible": false, "EPSEstimateNextQuarter": "%%OBJNAME?%%EPSEstimateNextQuarter.visible": false, "EPSEstimateCurrentQuarter": "%%OBJNAME?%%EPSEstimateCurrentQuarter.visible": false, "MostRecentQuarter": "%%OBJNAME?%%MostRecentQuarter.visible": false, "ProfitMargin": "%%OBJNAME?%%ProfitMargin.visible": false, "OperatingMarginTTM": "%%OBJNAME?%%OperatingMarginTTM.visible": false, "ReturnOnAssetsTTM": "%%OBJNAME?%%ReturnOnAssetsTTM.visible": false, "ReturnOnEquityTTM": "%%OBJNAME?%%ReturnOnEquityTTM.visible": false, "RevenueTTM": "%%OBJNAME?%%RevenueTTM.visible": false, "RevenuePerShareTTM": "%%OBJNAME?%%RevenuePerShareTTM.visible": false, "QuarterlyRevenueGrowthYOY": "%%OBJNAME?%%QuarterlyRevenueGrowthYOY.visible": false, "GrossProfitTTM": "%%OBJNAME?%%GrossProfitTTM.visible": false, "DilutedEpsTTM": "%%OBJNAME?%%DilutedEpsTTM.visible": false, "QuarterlyEarningsGrowthYOY": "%%OBJNAME?%%QuarterlyEarningsGrowthYOY.visible": false, "Shape.visible": false, "bgshape.visible": false }, "?": { "earningsimpactid": "%%OBJNAME?%%earningsimpactid.visible": false, "sample_size": "%%OBJNAME?%%sample_size.visible": false, "impactcompany": "%%OBJNAME?%%impactcompany.visible": false, "impactcompany_name": "%%OBJNAME?%%impactcompany_name.visible": false, "impactcompany_sector": "%%OBJNAME?%%impactcompany_sector.visible": false, "impactcompany_industry": "%%OBJNAME?%%impactcompany_industry.visible": false, "opportunities": "%%OBJNAME?%%opportunities.visible": false, "Code": "%%OBJNAME?%%Code.visible": false, "Type": "%%OBJNAME?%%Type.visible": false, "Name": "%%OBJNAME?%%Name.visible": false, "Exchange": "%%OBJNAME?%%Exchange.visible": false, "CurrencyCode": "%%OBJNAME?%%CurrencyCode.visible": false, "CurrencyName": "%%OBJNAME?%%CurrencyName.visible": false, "CurrencySymbol": "%%OBJNAME?%%CurrencySymbol.visible": false, "CountryName": "%%OBJNAME?%%CountryName.visible": false, "CountryISO": "%%OBJNAME?%%CountryISO.visible": false, "OpenFigi": "%%OBJNAME?%%OpenFigi.visible": false, "ISIN": "%%OBJNAME?%%ISIN.visible": false, "LEI": "%%OBJNAME?%%LEI.visible": false, "PrimaryTicker": "%%OBJNAME?%%PrimaryTicker.visible": false, "CUSIP": "%%OBJNAME?%%CUSIP.visible": false, "CIK": "%%OBJNAME?%%CIK.visible": false, "EmployerIdNumber": "%%OBJNAME?%%EmployerIdNumber.visible": false, "FiscalYearEnd": "%%OBJNAME?%%FiscalYearEnd.visible": false, "IPODate": "%%OBJNAME?%%IPODate.visible": false, "InternationalDomestic": "%%OBJNAME?%%InternationalDomestic.visible": false, "Sector": "%%OBJNAME?%%Sector.visible": false, "Industry": "%%OBJNAME?%%Industry.visible": false, "GicSector": "%%OBJNAME?%%GicSector.visible": false, "GicGroup": "%%OBJNAME?%%GicGroup.visible": false, "GicIndustry": "%%OBJNAME?%%GicIndustry.visible": false, "GicSubIndustry": "%%OBJNAME?%%GicSubIndustry.visible": false, "HomeCategory": "%%OBJNAME?%%HomeCategory.visible": false, "IsDelisted": "%%OBJNAME?%%IsDelisted.visible": false, "Description": "%%OBJNAME?%%Description.visible": false, "Address": "%%OBJNAME?%%Address.visible": false, "Phone": "%%OBJNAME?%%Phone.visible": false, "WebURL": "%%OBJNAME?%%WebURL.visible": false, "LogoURL": "%%OBJNAME?%%LogoURL.visible": false, "FullTimeEmployees": "%%OBJNAME?%%FullTimeEmployees.visible": false, "UpdatedAt": "%%OBJNAME?%%UpdatedAt.visible": false, "MarketCapitalization": "%%OBJNAME?%%MarketCapitalization.visible": false, "MarketCapitalizationMln": "%%OBJNAME?%%MarketCapitalizationMln.visible": false, "EBITDA": "%%OBJNAME?%%EBITDA.visible": false, "PERatio": "%%OBJNAME?%%PERatio.visible": false, "PEGRatio": "%%OBJNAME?%%PEGRatio.visible": false, "WallStreetTargetPrice": "%%OBJNAME?%%WallStreetTargetPrice.visible": false, "BookValue": "%%OBJNAME?%%BookValue.visible": false, "DividendShare": "%%OBJNAME?%%DividendShare.visible": false, "DividendYield": "%%OBJNAME?%%DividendYield.visible": false, "EarningsShare": "%%OBJNAME?%%EarningsShare.visible": false, "EPSEstimateCurrentYear": "%%OBJNAME?%%EPSEstimateCurrentYear.visible": false, "EPSEstimateNextYear": "%%OBJNAME?%%EPSEstimateNextYear.visible": false, "EPSEstimateNextQuarter": "%%OBJNAME?%%EPSEstimateNextQuarter.visible": false, "EPSEstimateCurrentQuarter": "%%OBJNAME?%%EPSEstimateCurrentQuarter.visible": false, "MostRecentQuarter": "%%OBJNAME?%%MostRecentQuarter.visible": false, "ProfitMargin": "%%OBJNAME?%%ProfitMargin.visible": false, "OperatingMarginTTM": "%%OBJNAME?%%OperatingMarginTTM.visible": false, "ReturnOnAssetsTTM": "%%OBJNAME?%%ReturnOnAssetsTTM.visible": false, "ReturnOnEquityTTM": "%%OBJNAME?%%ReturnOnEquityTTM.visible": false, "RevenueTTM": "%%OBJNAME?%%RevenueTTM.visible": false, "RevenuePerShareTTM": "%%OBJNAME?%%RevenuePerShareTTM.visible": false, "QuarterlyRevenueGrowthYOY": "%%OBJNAME?%%QuarterlyRevenueGrowthYOY.visible": false, "GrossProfitTTM": "%%OBJNAME?%%GrossProfitTTM.visible": false, "DilutedEpsTTM": "%%OBJNAME?%%DilutedEpsTTM.visible": false, "QuarterlyEarningsGrowthYOY": "%%OBJNAME?%%QuarterlyEarningsGrowthYOY.visible": false, "Shape.visible": false, "bgshape.visible": false } } }, "text": { "title": "\u062a\u0623\u062b\u06cc\u0631 \u0627\u0646\u062a\u0634\u0627\u0631 \u06af\u0632\u0627\u0631\u0634 \u0633\u0648\u062f Visa Inc. Class A (V)", "short_text": "Visa Inc. Class A (V) \u0627\u0645\u0631\u0648\u0632 \u06af\u0632\u0627\u0631\u0634 \u0633\u0648\u062f \u062e\u0648\u062f \u0631\u0627 \u0645\u0646\u062a\u0634\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f \u0648 \u0628\u0631 \u0633\u0647\u0627\u0645 \u0632\u06cc\u0631 \u062a\u0623\u062b\u06cc\u0631 \u062e\u0648\u0627\u0647\u062f \u06af\u0630\u0627\u0634\u062a:\n- Synchrony Financial (SYF)\n- Mastercard Inc (MA)\n- Capital One Financial Corporation (COF)\n- American Express Company (AXP)\n- PayPal Holdings Inc (PYPL)\n", "long_text": "\u0634\u0631\u06a9\u062a Visa Inc. Class A (V) \u0627\u0645\u0631\u0648\u0632 \u06af\u0632\u0627\u0631\u0634 \u062f\u0631\u0622\u0645\u062f \u062e\u0648\u062f \u0631\u0627 \u0645\u0646\u062a\u0634\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f.\n\u0648\u0642\u062a\u06cc \u0633\u0648\u062f \u0647\u0631 \u0633\u0647\u0645 (EPS) > \u0627\u0632 \u0645\u0642\u062f\u0627\u0631 \u067e\u06cc\u0634 \u200c\u0628\u06cc\u0646\u06cc\u200c \u0634\u062f\u0647 \u0628\u0627\u0634\u062f\u060c \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70%\u200e\u200f \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u06a9\u0647 V \u062f\u0631 \u0645\u0627\u0647 \u067e\u0633 \u0627\u0632 \u0627\u0646\u062a\u0634\u0627\u0631 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u060c \u0628\u0647\u200c \u0637\u0648\u0631 \u0645\u06cc\u0627\u0646\u06af\u06cc\u0646 \u200f\u200e ?%\u200e\u200f \u0646\u0648\u0633\u0627\u0646 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.\n\u062f\u0631 \u06af\u0632\u0627\u0631\u0634\u200c\u0647\u0627\u06cc \u062f\u0631\u0622\u0645\u062f\u06cc \u06af\u0630\u0634\u062a\u0647 \u06a9\u0647 EPS > \u0627\u0632 \u067e\u06cc\u0634\u200c \u0628\u06cc\u0646\u06cc\u0647\u0627 \u0628\u0648\u062f\u0647 \u0627\u0633\u062a\u060c \u0633\u0647\u0627\u0645\u200c \u0647\u0627\u06cc \u0632\u06cc\u0631 \u062a\u062d\u062a \u062a\u0623\u062b\u06cc\u0631 \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0647\u200c\u0627\u0646\u062f:\n - Synchrony Financial \uff08SYF\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e13\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - Mastercard Inc \uff08MA\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e ?\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - Capital One Financial Corporation \uff08COF\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e80\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e ?\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - American Express Company \uff08AXP\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e12\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - PayPal Holdings Inc \uff08PYPL\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e\u20138\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n" }, "warnings": [], "errors": [], "has_results": true, "quantity_results": ?, "creatomate_response": [ { "id": "bf850582-526d-4a54-bb71-27a6036149e6", "status": "planned", "url": "https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/bf850582-526d-4a54-bb71-27a6036149e6.png", "template_id": "f571abff-6373-433f-895d-23d03056d1b6", "template_name": "FA - Earnings Impact And Sector Influence - ?", "template_tags": [], "output_format": "png" } ], "image_api": { "latest": [ "https://api.autochartist.com/social_media/image/?e56597a-f784-43e4-a140-8b32efacb73c?broker_id=?&item=?" ], "snapshot": [ "https://api.autochartist.com/social_media/image/?e56597a-f784-43e4-a140-8b32efacb73c?broker_id=?&item=?&dt=?-01-27%?%?A?%?A?" ] }, "webhook_response": { "attempt": "?bfe7c-539f-5640-4f0b-2d2f1727dabd", "id": "?bfe7c-539f-5640-4f0b-2d2f1727dabd", [...];

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

18

0ms

1

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing; insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

19

0ms

1

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

20

0ms

287

Details

0ms

with rar_max as ( select resultuid from relevance_fibonacci_results order by resultuid desc limit ? ), fr as ( select a.*, rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end ), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp))) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Jan 27	10	287	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

35,870

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Jan 27	10	35,870	0ms	0ms

2

20,887

Details

0ms

select distinct on (coalesce(bim.code, s.symbol) , s.exchange, s.timegranularity, df.timezone) s.symbolid as id, coalesce(bim.code, s.symbol) as name, s.symbol as symbol, dss.downloadersymbol as ticker, s.exchange as exchange, s.timegranularity as interval, df.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable df on df.classname ilike dss.classname left join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = s.symbolid left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = ? and bim.type = ? where s.symbolid = ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Jan 27	10	20,887	0ms	0ms

3

9,723

Details

0ms

select s.symbolid as id, s.symbol as name, s.exchange as exchange, s.timegranularity as interval, dtt.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join brokersymbollist bsl on bsl.symbolid = s.symbolid where bsl.brokerid = ? and (? = ? or s.timegranularity = ?) and (s.symbol = ? or dss.downloadersymbol = ?) and dss.enabled = ?;

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Jan 27	10	9,723	0ms	0ms

4

7,918

Details

0ms

insert into executionlogs (executionid, status, message, details, detailtype) values (null, ?, ?, null, null);

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Jan 27	10	7,918	0ms	0ms

5

5,795

Details

0ms

insert into t15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Jan 27	10	5,795	0ms	0ms

6

4,750

Details

0ms

insert into autochartist_results (resultid, symbolid, bandwidth, pattern, qtytp, gmttimefound, direction, initialtrend, breakout, volumeincrease, noise, symmetry, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimeto, patternstarttime, patternendtime, patternstartprice, patternendprice, resx0, resx1, supportx0, supportx1, resy0, resy1, supporty0, supporty1, supportgradient, resgradient, riskreward, patternquality, trendchange, maxmovementafterbreakout, latestbaratbreakouttime, latestbaratbreakoutprice, patternlengthbars, temporarypattern, relevancestartdistance, simulation, writtendatetime) values (?, ?, ?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Jan 27	10	4,750	0ms	0ms

7

4,158

Details

0ms

with rar_max as ( select resultuid from relevance_autochartist_results order by resultuid desc limit ? ) select a.symbolid, pattern, patternid, resy0, resy1, resx0, resx1, supporty0, supporty1, supportx0, supportx1, predictiontimeto, patternstarttime, timegranularity, patternendtime, direction, trendchange, patternlengthbars, patternquality, case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, s.exchange, s.symbol, s.longname, s.shortname, breakout, dtt.timezone, patternstartprice, patternendprice, qtytp, newlevels.profit, newlevels.stop, newlevels.filtered, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern inner join rar_max rm on ? = ? left outer join relevance_autochartist_results rar on rar.resultuid = a.resultuid left join lateral calc_cp_signal (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Jan 27	10	4,158	0ms	0ms

8

3,641

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Jan 27	10	3,641	0ms	0ms

9

3,608

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Jan 27	10	3,608	0ms	0ms

10

3,596

Details

0ms

insert into keylevels_results (bandwidth, breakout, patternid, gmttimefound, approachingtimestamp, approachingregion, qtytp, patternlengthbars, patternprice, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, breakoutbars, breakoutprice, patternendtime, atbaridentified, atpriceidentified, errormargin, direction, symbolid, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimebars, uniquepointsvalue, furthestprice, relevancestartdistance, patternclassid, patternstarttime, stoplosslevel, simulation, writtendatetime) values (?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?, ?, ?.?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?.?, ?::timestamp without time zone, ?, ?.?, ?.?, ?, ?, ?.?, ?.?, ?::timestamp without time zone, ?, ?, ?.?, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Jan 27	10	3,596	0ms	0ms

11

3,375

Details

0ms

insert into t30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Jan 27	10	3,375	0ms	0ms

12

3,282

Details

0ms

update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Jan 27	10	3,282	0ms	0ms

13

2,334

Details

0ms

insert into fibonacci_results (bandwidth, pattern, gmttimefound, direction, patternstarttime, patternendtime, patternstartprice, patternendprice, qtytp, pricex, timex, pricea, timea, priceb, timeb, pricec, timec, priced, timed, averagequality, timequality, errormargin, patternlengthbars, target10, target06, target16, target07, target12, target05, target03, symbolid, noise, ratiosfound, temporarypattern, uniqueindex, completed, simulation, writtendatetime) values (?.?, ?, ?::timestamp without time zone, ?, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?, ?, ?, ?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Jan 27	10	2,334	0ms	0ms

14

2,315

Details

0ms

with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ) select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as ruid, s.symbolid as sid, s.symbol as sym, longname, shortname, exchange as e, timegranularity as tg, a.patternid as pid, a.direction as d, a.patternprice as pp, atbaridentified as pet, case when (x9 != ?) then x9 when (x8 != ?) then x8 when (x7 != ?) then x7 when (x6 != ?) then x6 when (x5 != ?) then x5 when (x4 != ?) then x4 when (x3 != ?) then x3 when (x2 != ?) then x2 end as pst, patternprice as patp, x0, x1, x2, case when (x3 != ?) then x3 else ? end as x3, case when (x4 != ?) then x4 else ? end as x4, case when (x5 != ?) then x5 else ? end as x5, case when (x6 != ?) then x6 else ? end as x6, case when (x7 != ?) then x7 else ? end as x7, case when (x8 != ?) then x8 else ? end as x8, errormargin as erm, breakoutprice as pe, breakoutbars as be, breakout, atbaridentified as atbar, atpriceidentified as atprice, patternlengthbars as l, bandwidth as bw, qtytp as qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzos, dtt.timezone as timezone, approachingtimestamp as apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb, furthestprice as fp, newlevels.filtered, a.uniquepointsvalue as upv, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid inner join rar_max rm on ? = ? left outer join relevance_keylevels_results rar on a.resultuid = rar.resultuid left join lateral calc_kl_signal_filter (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Jan 27	10	2,315	0ms	0ms

15

2,155

Details

0ms

insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Jan 27	10	2,155	0ms	0ms

16

978

Details

0ms

select symbolid, pricedatetime, classname, downloadfrequency, downloadersymbol, open, high, low, close, volume, bsf, sastdatetimereceived from ( select pricedatetime, dss.classname, dss.downloadfrequency, dss.symbolid, dss.downloadersymbol, t.open, t.high, t.low, t.close, t.volume, t.bsf, t.sastdatetimereceived, row_number() over (partition by t.symbolid order by t.pricedatetime desc) as rn from t15 t, downloadersymbolsettings dss, symbols s where dss.classname = ? and dss.downloadfrequency = ? and dss.symbolid = t.symbolid and s.symbolid = dss.symbolid and dss.enabled = ? and s.deleted = ? and dss.downloadersymbol in (...) and t.pricedatetime > now() - interval ?) as ranked_candles_table where rn = ?;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Jan 27	10	978	0ms	0ms

17

913

Details

0ms

select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_summary where statsid = ? and category = lower(?) union select category, name, total, correct, percentage, "from", "to" from stats_hrs_summary where statsid = ? and category = lower(?) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Jan 27	10	913	0ms	0ms

18

641

Details

0ms

select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, pattern as patternname, timegranularity as interval, patternlengthbars as length, patternendtime, direction, breakout, predictiontimeto, predictionpricefrom, predictionpriceto, patternstartprice, resy1, supporty1, dtt.timezone, cps.pip, newlevels.profit from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern left join currencypips cps on cps.symbol = s.symbol left join lateral calc_cp_signal (a.resultuid) newlevels on true where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Jan 27	10	641	0ms	0ms

19

529

Details

0ms

select downloadersymbol, spike_threshold from price_datafeed_spike_threshold where classname = ?;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Jan 27	10	529	0ms	0ms

20

529

Details

0ms

select s.symbolid, dss.downloadfrequency, dss.downloadersymbol from downloadersymbolsettings dss inner join symbols s on dss.symbolid = s.symbolid where dss.classname = ? and s.deleted = ? and dss.enabled = ?;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Jan 27	10	529	0ms	0ms

Normalized slowest queries (N)

Rank

Min duration

Max duration

Avg duration

Times executed

Total duration

Query

1

0ms

1

Details

0ms

select s.statsid, s.description, s.groupingname, cast(ss.percentage as integer) as cppercentage, cast(hss.percentage as integer) as klpercentage, cast(hass.percentage as integer) as aklpercentage, case when b.name ilike ? then ? else ? end as showaclogo from broker b inner join stats s on b.brokerid = s.brokerid left outer join stats_summary ss on ss.statsid = s.statsid left outer join stats_hrs_summary hss on hss.statsid = s.statsid left outer join stats_hrsapproaches_summary hass on hass.statsid = s.statsid where s.brokerid = ? and ss.total > ? and ss.category ilike ? and hss.category ilike ? and hass.category ilike ? group by s.statsid, s.description, s.brokerid, s.latestupdate, s.groupingname, s.calcfrom, s.calcto, ss.statsid, ss.percentage, hss.percentage, hass.percentage, b.name;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

2

0ms

38

Details

0ms

select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Jan 27	10	38	0ms	0ms

3

0ms

15

Details

0ms

with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Jan 27	10	15	0ms	0ms

4

0ms

2,155

Details

0ms

insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Jan 27	10	2,155	0ms	0ms

5

0ms

48

Details

0ms

select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Jan 27	10	48	0ms	0ms

6

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Jan 27	10	4	0ms	0ms

7

0ms

6

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Jan 27	10	6	0ms	0ms

8

0ms

6

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Jan 27	10	6	0ms	0ms

9

0ms

18

Details

0ms

select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Jan 27	10	18	0ms	0ms

10

0ms

434

Details

0ms

commit;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Jan 27	10	434	0ms	0ms

11

0ms

311

Details

0ms

with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ), kr as ( select a.*, rr.age, rr.relevant from keylevels_results a left outer join relevance_keylevels_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_keylevels_results) end ), all_results as ( select kr.resultuid as resultuid, kr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, p.patternname as pattern_name, kr.breakout as breakout, kr.atbaridentified as identified, dtt.timezone as timezone, kr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when kr.age is not null then kr.age when kr.resultuid <= rm.resultuid then ? else ? end as age, case when kr.relevant is not null then kr.relevant when kr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from kr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = kr.symbolid inner join symbols s on bsl.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join hrspatterns p on kr.patternid = p.patternid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left outer join autochartist_symbolupdates au on dss.symbolid = au.symbolid left outer join relevance_keylevels_results rar on rar.resultuid = kr.resultuid left join lateral calc_kl_signal_filter (kr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where kr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (kr.simulation = ? or kr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or p.patternname in (...)) and (? = ? or kr.patternclassid in (...)) and (? = ? or kr.patternlengthbars <= ?) and kr.patternstarttime::timestamp without time zone >= coalesce(au.earliestpricedatetime, ?::timestamp without time zone) -- to make sure patternstarttime is in our t-tables ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc limit ?;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Jan 27	10	311	0ms	0ms

12

0ms

240

Details

0ms

select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Jan 27	10	240	0ms	0ms

13

0ms

240

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Jan 27	10	240	0ms	0ms

14

0ms

5

Details

0ms

select groupid, exchange, groupname, symbol, longname from prfsymboltree where brokerid = ? order by groupname, symbol;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Jan 27	10	5	0ms	0ms

15

0ms

4

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Jan 27	10	4	0ms	0ms

16

0ms

11

Details

0ms

select updatedatafeedslatestrun (?);

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Jan 27	10	11	0ms	0ms

17

0ms

1

Details

0ms

update executions set isrunning = false, has_results=true, response=?'s products and services include credit card, charge card, banking, and other payment and financing products; network services; expense management products and services; and travel and lifestyle services. It also provides merchant acquisition and processing, servicing and settlement, point-of-sale marketing, and information products and services for merchants; and fraud prevention services, as well as the design and operation of customer loyalty programs. In addition, the company leases and operates lounges at airports. Further, it designs and develops a software to manage company expenses. The company sells its products and services to consumers, small businesses, mid-sized companies, and large corporations through mobile and online applications, affiliate marketing, customer referral programs, third-party service providers and business partners, direct mail, telephone, in-house sales teams, telephone, and direct response advertising. American Express Company was founded in ? and is based in New York, New York.", "Address": "? Vesey Street, New York, NY, United States, ?", "Phone": "(?) ?-2000", "WebURL": "https://www.americanexpress.com", "LogoURL": "https://eodhistoricaldata.com/img/logos/US/axp.png", "FullTimeEmployees": ?, "UpdatedAt": "?-01-27", "MarketCapitalization": ?, "MarketCapitalizationMln": ?.?, "EBITDA": null, "PERatio": ?.?, "PEGRatio": ?.?, "WallStreetTargetPrice": ?.?, "BookValue": ?.?, "DividendShare": ?.?, "DividendYield": ?.?, "EarningsShare": ?.?, "EPSEstimateCurrentYear": ?.?, "EPSEstimateNextYear": ?.?, "EPSEstimateNextQuarter": ?.?, "EPSEstimateCurrentQuarter": ?.?, "MostRecentQuarter": "?-09-30", "ProfitMargin": ?.?, "OperatingMarginTTM": ?.?, "ReturnOnAssetsTTM": ?.?, "ReturnOnEquityTTM": ?.?, "RevenueTTM": ?, "RevenuePerShareTTM": ?.?, "QuarterlyRevenueGrowthYOY": ?.?, "GrossProfitTTM": ?, "DilutedEpsTTM": ?.?, "QuarterlyEarningsGrowthYOY": ?.? }, "?": { "earningsimpactid": ?, "sample_size": ?, "impactcompany": "PYPL", "impactcompany_name": "PayPal Holdings Inc", "impactcompany_sector": "Financial Services", "impactcompany_industry": "Credit Services", "opportunities": [ { "forecastperiod_trading_days": ?, "id": "delta_gt_down", "probability": "?%", "delta_gt": ?, "delta_gt_down": ?, "trend_direction": ?, "delta_sign": ">", "mean_mov": "?.?", "mean_mov_percent": "?%", "icon": "https://acarrows.s3.eu-west-1.amazonaws.com/arrows/down.svg" } ], "Code": "PYPL", "Type": "Common Stock", "Name": "PayPal Holdings Inc", "Exchange": "NASDAQ", "CurrencyCode": "USD", "CurrencyName": "US Dollar", "CurrencySymbol": "$", "CountryName": "USA", "CountryISO": "US", "OpenFigi": "BBG?VNXV?", "ISIN": "US?Y?", "LEI": "?X?GO?EFZ?E?", "PrimaryTicker": "PYPL.US", "CUSIP": "?Y?", "CIK": "?", "EmployerIdNumber": "?-2989869", "FiscalYearEnd": "December", "IPODate": "?-02-01", "InternationalDomestic": "Domestic", "Sector": "Financial Services", "Industry": "Credit Services", "GicSector": "Financials", "GicGroup": "Financial Services", "GicIndustry": "Financial Services", "GicSubIndustry": "Transaction & Payment Processing Services", "HomeCategory": "Domestic", "IsDelisted": false, "Description": "PayPal Holdings, Inc. operates a technology platform that enables digital payments for merchants and consumers worldwide. It operates a two-sided network at scale that connects merchants and consumers that enables its customers to connect, transact, and send and receive payments through online and in person, as well as transfer and withdraw funds using various funding sources, such as bank accounts, PayPal or Venmo account balance, consumer credit products, credit and debit cards, and cryptocurrencies, as well as other stored value products, including gift cards and eligible rewards. The company provides payment solutions under the PayPal, PayPal Credit, Braintree, Venmo, Xoom, Zettle, Hyperwallet, Honey, and Paidy names. The company was founded in ? and is headquartered in San Jose, California.", "Address": "? North First Street, San Jose, CA, United States, ?", "Phone": "? ? ?", "WebURL": "https://www.paypal.com", "LogoURL": "https://eodhistoricaldata.com/img/logos/US/pypl.png", "FullTimeEmployees": ?, "UpdatedAt": "?-01-26", "MarketCapitalization": ?, "MarketCapitalizationMln": ?.?, "EBITDA": ?, "PERatio": ?.?, "PEGRatio": ?.?, "WallStreetTargetPrice": ?.?, "BookValue": ?.?, "DividendShare": ?.?, "DividendYield": ?.?, "EarningsShare": ?.?, "EPSEstimateCurrentYear": ?.?, "EPSEstimateNextYear": ?.?, "EPSEstimateNextQuarter": ?.?, "EPSEstimateCurrentQuarter": ?.?, "MostRecentQuarter": "?-09-30", "ProfitMargin": ?.?, "OperatingMarginTTM": ?.?, "ReturnOnAssetsTTM": ?.?, "ReturnOnEquityTTM": ?.?, "RevenueTTM": ?, "RevenuePerShareTTM": ?.?, "QuarterlyRevenueGrowthYOY": ?.?, "GrossProfitTTM": ?, "DilutedEpsTTM": ?.?, "QuarterlyEarningsGrowthYOY": ?.? }, "?": { "earningsimpactid": "%%OBJNAME?%%earningsimpactid.visible": false, "sample_size": "%%OBJNAME?%%sample_size.visible": false, 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"%%OBJNAME?%%PrimaryTicker.visible": false, "CUSIP": "%%OBJNAME?%%CUSIP.visible": false, "CIK": "%%OBJNAME?%%CIK.visible": false, "EmployerIdNumber": "%%OBJNAME?%%EmployerIdNumber.visible": false, "FiscalYearEnd": "%%OBJNAME?%%FiscalYearEnd.visible": false, "IPODate": "%%OBJNAME?%%IPODate.visible": false, "InternationalDomestic": "%%OBJNAME?%%InternationalDomestic.visible": false, "Sector": "%%OBJNAME?%%Sector.visible": false, "Industry": "%%OBJNAME?%%Industry.visible": false, "GicSector": "%%OBJNAME?%%GicSector.visible": false, "GicGroup": "%%OBJNAME?%%GicGroup.visible": false, "GicIndustry": "%%OBJNAME?%%GicIndustry.visible": false, "GicSubIndustry": "%%OBJNAME?%%GicSubIndustry.visible": false, "HomeCategory": "%%OBJNAME?%%HomeCategory.visible": false, "IsDelisted": "%%OBJNAME?%%IsDelisted.visible": false, "Description": "%%OBJNAME?%%Description.visible": false, "Address": "%%OBJNAME?%%Address.visible": false, "Phone": "%%OBJNAME?%%Phone.visible": false, "WebURL": 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"%%OBJNAME?%%QuarterlyRevenueGrowthYOY.visible": false, "GrossProfitTTM": "%%OBJNAME?%%GrossProfitTTM.visible": false, "DilutedEpsTTM": "%%OBJNAME?%%DilutedEpsTTM.visible": false, "QuarterlyEarningsGrowthYOY": "%%OBJNAME?%%QuarterlyEarningsGrowthYOY.visible": false, "Shape.visible": false, "bgshape.visible": false } } }, "text": { "title": "\u062a\u0623\u062b\u06cc\u0631 \u0627\u0646\u062a\u0634\u0627\u0631 \u06af\u0632\u0627\u0631\u0634 \u0633\u0648\u062f Visa Inc. Class A (V)", "short_text": "Visa Inc. Class A (V) \u0627\u0645\u0631\u0648\u0632 \u06af\u0632\u0627\u0631\u0634 \u0633\u0648\u062f \u062e\u0648\u062f \u0631\u0627 \u0645\u0646\u062a\u0634\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f \u0648 \u0628\u0631 \u0633\u0647\u0627\u0645 \u0632\u06cc\u0631 \u062a\u0623\u062b\u06cc\u0631 \u062e\u0648\u0627\u0647\u062f \u06af\u0630\u0627\u0634\u062a:\n- Synchrony Financial (SYF)\n- Mastercard Inc (MA)\n- Capital One Financial Corporation (COF)\n- American Express Company (AXP)\n- PayPal Holdings Inc (PYPL)\n", "long_text": "\u0634\u0631\u06a9\u062a Visa Inc. Class A (V) \u0627\u0645\u0631\u0648\u0632 \u06af\u0632\u0627\u0631\u0634 \u062f\u0631\u0622\u0645\u062f \u062e\u0648\u062f \u0631\u0627 \u0645\u0646\u062a\u0634\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f.\n\u0648\u0642\u062a\u06cc \u0633\u0648\u062f \u0647\u0631 \u0633\u0647\u0645 (EPS) > \u0627\u0632 \u0645\u0642\u062f\u0627\u0631 \u067e\u06cc\u0634 \u200c\u0628\u06cc\u0646\u06cc\u200c \u0634\u062f\u0647 \u0628\u0627\u0634\u062f\u060c \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70%\u200e\u200f \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u06a9\u0647 V \u062f\u0631 \u0645\u0627\u0647 \u067e\u0633 \u0627\u0632 \u0627\u0646\u062a\u0634\u0627\u0631 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u060c \u0628\u0647\u200c \u0637\u0648\u0631 \u0645\u06cc\u0627\u0646\u06af\u06cc\u0646 \u200f\u200e ?%\u200e\u200f \u0646\u0648\u0633\u0627\u0646 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.\n\u062f\u0631 \u06af\u0632\u0627\u0631\u0634\u200c\u0647\u0627\u06cc \u062f\u0631\u0622\u0645\u062f\u06cc \u06af\u0630\u0634\u062a\u0647 \u06a9\u0647 EPS > \u0627\u0632 \u067e\u06cc\u0634\u200c \u0628\u06cc\u0646\u06cc\u0647\u0627 \u0628\u0648\u062f\u0647 \u0627\u0633\u062a\u060c \u0633\u0647\u0627\u0645\u200c \u0647\u0627\u06cc \u0632\u06cc\u0631 \u062a\u062d\u062a \u062a\u0623\u062b\u06cc\u0631 \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0647\u200c\u0627\u0646\u062f:\n - Synchrony Financial \uff08SYF\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e13\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - Mastercard Inc \uff08MA\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e ?\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - Capital One Financial Corporation \uff08COF\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e80\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e ?\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - American Express Company \uff08AXP\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e12\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n - PayPal Holdings Inc \uff08PYPL\uff09\uff1a \u0627\u062d\u062a\u0645\u0627\u0644 \u200f\u200e70\u066a\u200e\u200f \u0628\u0631\u0627\u06cc \u062d\u0631\u06a9\u062a \u200f\u200e\u20138\u066a\u200e\u200f \u062f\u0631 \u0645\u0627\u0647 \u0628\u0639\u062f \u0627\u0632 \u0627\u06cc\u0646 \u06af\u0632\u0627\u0631\u0634\u061c\u06d4\n" }, "warnings": [], "errors": [], "has_results": true, "quantity_results": ?, "creatomate_response": [ { "id": "bf850582-526d-4a54-bb71-27a6036149e6", "status": "planned", "url": "https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/bf850582-526d-4a54-bb71-27a6036149e6.png", "template_id": "f571abff-6373-433f-895d-23d03056d1b6", "template_name": "FA - Earnings Impact And Sector Influence - ?", "template_tags": [], "output_format": "png" } ], "image_api": { "latest": [ "https://api.autochartist.com/social_media/image/?e56597a-f784-43e4-a140-8b32efacb73c?broker_id=?&item=?" ], "snapshot": [ "https://api.autochartist.com/social_media/image/?e56597a-f784-43e4-a140-8b32efacb73c?broker_id=?&item=?&dt=?-01-27%?%?A?%?A?" ] }, "webhook_response": { "attempt": "?bfe7c-539f-5640-4f0b-2d2f1727dabd", "id": "?bfe7c-539f-5640-4f0b-2d2f1727dabd", [...];

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

18

0ms

1

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing; insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

19

0ms

1

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Jan 27	10	1	0ms	0ms

20

0ms

287

Details

0ms

with rar_max as ( select resultuid from relevance_fibonacci_results order by resultuid desc limit ? ), fr as ( select a.*, rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end ), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp))) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Jan 27	10	287	0ms	0ms

Time consuming prepare


Date: 2026-01-27 10:30:58
Duration: 13ms
Database: postgres
Date: 2026-01-27 10:59:48
Duration: 9ms
Database: postgres
Date: 2026-01-27 10:31:00
Duration: 4ms
Database: postgres
Date: 2026-01-27 10:45:51
Duration: 11ms
Database: postgres
Date: 2026-01-27 10:41:20
Duration: 4ms
Database: postgres
Date: 2026-01-27 10:31:01
Duration: 1ms
Database: postgres
Date: 2026-01-27 10:42:50
Duration: 8ms
Database: postgres
Date: 2026-01-27 10:02:41
Duration: 0ms
Database: postgres
Date: 2026-01-27 10:15:42
Duration: 0ms
Database: postgres
Date: 2026-01-27 10:06:45
Duration: 7ms
Database: postgres
Date: 2026-01-27 10:25:53
Duration: 1ms
Database: postgres
Date: 2026-01-27 10:30:57
Duration: 1ms
Database: postgres
Date: 2026-01-27 10:16:01
Duration: 3ms
Database: postgres
Date: 2026-01-27 10:11:01
Duration: 3ms
Database: postgres
Date: 2026-01-27 10:41:20
Duration: 2ms
Database: postgres
Date: 2026-01-27 10:13:24
Duration: 0ms
Database: postgres
Date: 2026-01-27 10:25:53
Duration: 0ms
Database: postgres
Date: 2026-01-27 10:10:05
Duration: 3ms
Database: postgres
Date: 2026-01-27 10:00:02
Duration: 3ms
Database: postgres
Date: 2026-01-27 10:06:06
Duration: 0ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

39s548ms

8,438

Details

0ms

41ms

4ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Jan 27	10	8,438	39s548ms	4ms

WITH rar_max as ( ;

Date: 2026-01-27 10:01:15 Duration: 41ms Database: postgres parameters: $1 = '607574678456599301', $2 = '607574678456599301', $3 = '607574678456599301'
WITH rar_max as ( ;

Date: 2026-01-27 10:01:15 Duration: 38ms Database: postgres parameters: $1 = '607573618103200301', $2 = '607573618103200301', $3 = '607573618103200301'
WITH rar_max as ( ;

Date: 2026-01-27 10:31:01 Duration: 37ms Database: postgres parameters: $1 = 't', $2 = '627', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '213', $14 = '#ADBE', $15 = '#ALVG', $16 = '#AMZN', $17 = '#APPL', $18 = '#BA', $19 = '#BABA', $20 = '#BAYGn', $21 = '#BMWG', $22 = '#BNPP', $23 = '#CAT', $24 = '#CBKG', $25 = '#DAIGn', $26 = '#DIS', $27 = '#EA', $28 = '#FB', $29 = '#FDX', $30 = '#GE', $31 = '#GM', $32 = '#GOOGL', $33 = '#GS', $34 = '#INTC', $35 = '#JPM', $36 = '#KO', $37 = '#META', $38 = '#MSFT', $39 = '#NFLX', $40 = '#TSLA', $41 = '#VOWG', $42 = '#WMT', $43 = '#XOM', $44 = 'AUDCAD', $45 = 'AUDCHF', $46 = 'AUDJPY', $47 = 'AUDNZD', $48 = 'AUDUSD', $49 = 'AUS_200', $50 = 'BTCEUR', $51 = 'BTCGBP', $52 = 'BTCUSD', $53 = 'CADCHF', $54 = 'CADJPY', $55 = 'CHFJPY', $56 = 'CL_BRENT', $57 = 'DASHUSD', $58 = 'EOSUSD', $59 = 'ESP_35', $60 = 'ETHEUR', $61 = 'ETHGBP', $62 = 'ETHUSD', $63 = 'EURAUD', $64 = 'EURCAD', $65 = 'EURCHF', $66 = 'EURGBP', $67 = 'EURJPY', $68 = 'EURMXN', $69 = 'EURNOK', $70 = 'EURNZD', $71 = 'EURPLN', $72 = 'EURSEK', $73 = 'EURTRY', $74 = 'EURUSD', $75 = 'EUR_50', $76 = 'FRA_40', $77 = 'GBPAUD', $78 = 'GBPCAD', $79 = 'GBPCHF', $80 = 'GBPJPY', $81 = 'GBPNZD', $82 = 'GBPUSD', $83 = 'GBPZAR', $84 = 'GBR_100', $85 = 'HKDJPY', $86 = 'HKG_50', $87 = 'IOTAUSD', $88 = 'LTCEUR', $89 = 'LTCUSD', $90 = 'NAS100', $91 = 'NEOUSD', $92 = 'NOKJPY', $93 = 'NZDCAD', $94 = 'NZDCHF', $95 = 'NZDJPY', $96 = 'NZDUSD', $97 = 'OMGUSD', $98 = 'SPX500', $99 = 'TRXUSD', $100 = 'US30', $101 = 'USDCAD', $102 = 'USDCHF', $103 = 'USDCNH', $104 = 'USDDKK', $105 = 'USDJPY', $106 = 'USDMXN', $107 = 'USDNOK', $108 = 'USDPLN', $109 = 'USDSEK', $110 = 'USDSGD', $111 = 'USDZAR', $112 = 'USOIL', $113 = 'XAGUSD', $114 = 'XAUEUR', $115 = 'XAUUSD', $116 = 'XMRUSD', $117 = 'XPTUSD', $118 = 'XRPUSD', $119 = 'ZARJPY', $120 = 'ZECUSD', $121 = 'AUDCAD', $122 = 'AUDCHF', $123 = 'AUDJPY', $124 = 'AUDNZD', $125 = 'AUDUSD', $126 = 'CADCHF', $127 = 'CADJPY', $128 = 'CHFJPY', $129 = 'EURAUD', $130 = 'EURCAD', $131 = 'EURCHF', $132 = 'EURGBP', $133 = 'EURJPY', $134 = 'EURMXN', $135 = 'EURNOK', $136 = 'EURNZD', $137 = 'EURPLN', $138 = 'EURSEK', $139 = 'EURTRY', $140 = 'EURUSD', $141 = 'GBPAUD', $142 = 'GBPCAD', $143 = 'GBPCHF', $144 = 'GBPJPY', $145 = 'GBPNZD', $146 = 'GBPUSD', $147 = 'GBPZAR', $148 = 'HKDJPY', $149 = 'NOKJPY', $150 = 'NZDCAD', $151 = 'NZDCHF', $152 = 'NZDJPY', $153 = 'NZDUSD', $154 = 'USDCAD', $155 = 'USDCHF', $156 = 'USDCNH', $157 = 'USDDKK', $158 = 'USDJPY', $159 = 'USDMXN', $160 = 'USDNOK', $161 = 'USDPLN', $162 = 'USDSEK', $163 = 'USDSGD', $164 = 'USDZAR', $165 = 'ZARJPY', $166 = 'BTCEUR', $167 = 'BTCGBP', $168 = 'BTCUSD', $169 = 'DASHUSD', $170 = 'EOSUSD', $171 = 'ETHEUR', $172 = 'ETHGBP', $173 = 'ETHUSD', $174 = 'IOTAUSD', $175 = 'LTCEUR', $176 = 'LTCUSD', $177 = 'NEOUSD', $178 = 'OMGUSD', $179 = 'TRXUSD', $180 = 'XMRUSD', $181 = 'XRPUSD', $182 = 'ZECUSD', $183 = 'XAGUSD', $184 = 'XAUEUR', $185 = 'XAUUSD', $186 = 'XPTUSD', $187 = 'CL_BRENT', $188 = 'USOIL', $189 = '#ALVG', $190 = '#BAYGn', $191 = '#BMWG', $192 = '#BNPP', $193 = '#CBKG', $194 = '#DAIGn', $195 = '#VOWG', $196 = 'AUS_200', $197 = 'ESP_35', $198 = 'EUR_50', $199 = 'FRA_40', $200 = 'GBR_100', $201 = 'HKG_50', $202 = 'NAS100', $203 = 'SPX500', $204 = 'US30', $205 = '#ADBE', $206 = '#AMZN', $207 = '#APPL', $208 = '#BA', $209 = '#BABA', $210 = '#CAT', $211 = '#DIS', $212 = '#EA', $213 = '#FB', $214 = '#FDX', $215 = '#GE', $216 = '#GM', $217 = '#GOOGL', $218 = '#GS', $219 = '#INTC', $220 = '#JPM', $221 = '#KO', $222 = '#MSFT', $223 = '#NFLX', $224 = '#TSLA', $225 = '#WMT', $226 = '#XOM', $227 = '0', $228 = '', $229 = '0', $230 = '0', $231 = '0', $232 = '500', $233 = '500', $234 = 't', $235 = '10', $236 = '10'

2

15s667ms

34,350

Details

0ms

24ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	10	34,350	15s667ms	0ms

SELECT ;

Date: 2026-01-27 10:15:44 Duration: 24ms Database: postgres parameters: $1 = '958', $2 = '958', $3 = '515840243162686300'
SELECT ;

Date: 2026-01-27 10:16:14 Duration: 23ms Database: postgres parameters: $1 = '958', $2 = '958', $3 = '515840243162375300'
SELECT ;

Date: 2026-01-27 10:51:23 Duration: 17ms Database: postgres parameters: $1 = '958', $2 = '958', $3 = '515840243162375300'

3

2s545ms

1,102

Details

1ms

10ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	10	1,102	2s545ms	2ms

SELECT symbolid, ;

Date: 2026-01-27 10:45:51 Duration: 10ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '15', $3 = 'LTCUSD', $4 = 'NAS100', $5 = 'NEOUSD', $6 = 'IOTAUSD', $7 = 'LTCEUR'
SELECT symbolid, ;

Date: 2026-01-27 10:45:51 Duration: 5ms Database: postgres parameters: $1 = 'PEPPERSTONE', $2 = '15', $3 = 'GBPJPY', $4 = 'GBPMXN', $5 = 'GBPCHF', $6 = 'GBPAUD', $7 = 'GBPSGD', $8 = 'GBPNOK', $9 = 'GBPSEK', $10 = 'GBPNZD', $11 = 'GBPCAD'
SELECT symbolid, ;

Date: 2026-01-27 10:15:43 Duration: 4ms Database: postgres parameters: $1 = 'ATFX', $2 = '15', $3 = 'USDCHF', $4 = 'USDCNH', $5 = 'US30', $6 = 'USDCAD'

4

922ms

529

Details

1ms

2ms

1ms

SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	10	529	922ms	1ms

SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-01-27 10:16:02 Duration: 2ms Database: postgres parameters: $1 = 'HOTFOREX'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-01-27 10:30:44 Duration: 2ms Database: postgres parameters: $1 = 'ATFX'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-01-27 10:45:51 Duration: 2ms Database: postgres parameters: $1 = 'BDSWISS'

5

693ms

35,757

Details

0ms

13ms

0ms

select 1;

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	10	35,757	693ms	0ms

select 1;

Date: 2026-01-27 10:30:58 Duration: 13ms Database: postgres
select 1;

Date: 2026-01-27 10:57:47 Duration: 10ms Database: postgres
select 1;

Date: 2026-01-27 10:47:40 Duration: 9ms Database: postgres

6

595ms

23

Details

0ms

43ms

25ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	10	23	595ms	25ms

with wh_patitioned as ( ;

Date: 2026-01-27 10:10:01 Duration: 43ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
with wh_patitioned as ( ;

Date: 2026-01-27 10:00:01 Duration: 43ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
with wh_patitioned as ( ;

Date: 2026-01-27 10:41:51 Duration: 40ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

7

570ms

16

Details

28ms

46ms

35ms

with sym_info as ( ;

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	10	16	570ms	35ms

with sym_info as ( ;

Date: 2026-01-27 10:21:43 Duration: 46ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'
with sym_info as ( ;

Date: 2026-01-27 10:06:45 Duration: 46ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'
with sym_info as ( ;

Date: 2026-01-27 10:51:50 Duration: 44ms Database: postgres parameters: $1 = '627', $2 = 'Forex', $3 = 'Forex', $4 = '627', $5 = 'Forex', $6 = '627', $7 = '627', $8 = 'Forex', $9 = '627'

8

538ms

71

Details

4ms

20ms

7ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #8

Day	Hour	Count	Duration	Avg duration
	10	71	538ms	7ms

WITH last_candle AS ( ;

Date: 2026-01-27 10:28:02 Duration: 20ms Database: postgres parameters: $1 = '538', $2 = '538'
WITH last_candle AS ( ;

Date: 2026-01-27 10:32:00 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-01-27 10:16:01 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'

9

253ms

5,795

Details

0ms

INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	10	5,795	253ms	0ms

INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:30:54 Duration: 0ms Database: postgres parameters: $1 = '2026-01-27 10:15:00', $2 = '0.77811', $3 = '0.77831', $4 = '0.77773', $5 = '0.77797', $6 = '1015', $7 = '515840243869386300', $8 = '0', $9 = '2026-01-27 10:30:54.001', $10 = '2026-01-27 10:30:53.887', $11 = '0.77811', $12 = '0.77831', $13 = '0.77773', $14 = '0.77797', $15 = '1015', $16 = '0', $17 = '2026-01-27 10:30:54.001', $18 = '2026-01-27 10:30:53.887'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:31:03 Duration: 0ms Database: postgres parameters: $1 = '2026-01-27 11:15:00', $2 = '20.4433', $3 = '20.4603', $4 = '20.43895', $5 = '20.45465', $6 = '1638', $7 = '515840249385551300', $8 = '0', $9 = '2026-01-27 10:31:03.78', $10 = '2026-01-27 10:31:03.688', $11 = '20.4433', $12 = '20.4603', $13 = '20.43895', $14 = '20.45465', $15 = '1638', $16 = '0', $17 = '2026-01-27 10:31:03.78', $18 = '2026-01-27 10:31:03.688'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:17:34 Duration: 0ms Database: postgres parameters: $1 = '2026-01-27 10:00:00', $2 = '5086.115', $3 = '5088.4', $4 = '5079.41', $5 = '5086.645', $6 = '2867', $7 = '515840230628558300', $8 = '0', $9 = '2026-01-27 10:17:34.687', $10 = '2026-01-27 10:17:34.627', $11 = '5086.115', $12 = '5088.4', $13 = '5079.41', $14 = '5086.645', $15 = '2867', $16 = '0', $17 = '2026-01-27 10:17:34.687', $18 = '2026-01-27 10:17:34.627'

10

253ms

22

Details

0ms

21ms

11ms

WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	10	22	253ms	11ms

WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-01-27 10:22:08 Duration: 21ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-01-27 10:31:30 Duration: 13ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-01-27 10:07:09 Duration: 12ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

11

250ms

3,375

Details

0ms

2ms

0ms

INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #11

Day	Hour	Count	Duration	Avg duration
	10	3,375	250ms	0ms

INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:31:52 Duration: 2ms Database: postgres parameters: $1 = '2026-01-27 11:00:00', $2 = '1415138', $3 = '1418111.93', $4 = '1414675.56', $5 = '1416235.37', $6 = '7371', $7 = '515840249474000300', $8 = '0', $9 = '2026-01-27 10:31:52.176', $10 = '2026-01-27 10:31:52.175', $11 = '1415138', $12 = '1418111.93', $13 = '1414675.56', $14 = '1416235.37', $15 = '7371', $16 = '0', $17 = '2026-01-27 10:31:52.176', $18 = '2026-01-27 10:31:52.175'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:31:03 Duration: 1ms Database: postgres parameters: $1 = '2026-01-27 11:00:00', $2 = '49381.45', $3 = '49392.5', $4 = '49356', $5 = '49358.45', $6 = '2589', $7 = '515840249388074300', $8 = '0', $9 = '2026-01-27 10:31:03.793', $10 = '2026-01-27 10:31:03.793', $11 = '49381.45', $12 = '49392.5', $13 = '49356', $14 = '49358.45', $15 = '2589', $16 = '0', $17 = '2026-01-27 10:31:03.793', $18 = '2026-01-27 10:31:03.793'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:16:00 Duration: 0ms Database: postgres parameters: $1 = '2026-01-27 09:30:00', $2 = '8952.98', $3 = '8955.98', $4 = '8950.98', $5 = '8952.98', $6 = '260', $7 = '515840238059521300', $8 = '0', $9 = '2026-01-27 10:16:00.659', $10 = '2026-01-27 10:16:00.649', $11 = '8952.98', $12 = '8955.98', $13 = '8950.98', $14 = '8952.98', $15 = '260', $16 = '0', $17 = '2026-01-27 10:16:00.659', $18 = '2026-01-27 10:16:00.649'

12

176ms

38

Details

0ms

18ms

4ms

select distinct s.statsid as statsid, sy.exchange as name;

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	10	38	176ms	4ms

select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-01-27 10:00:59 Duration: 18ms Database: postgres parameters: $1 = '631', $2 = '631'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-01-27 10:01:00 Duration: 18ms Database: postgres parameters: $1 = '632', $2 = '632'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-01-27 10:00:34 Duration: 18ms Database: postgres parameters: $1 = '627', $2 = '627'

13

163ms

2,155

Details

0ms

INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #13

Day	Hour	Count	Duration	Avg duration
	10	2,155	163ms	0ms

INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:02:41 Duration: 0ms Database: postgres parameters: $1 = '2026-01-27 08:00:00', $2 = '8948.05', $3 = '8951.7', $4 = '8943.4', $5 = '8949.85', $6 = '1483', $7 = '515840248015562300', $8 = '0', $9 = '2026-01-27 10:02:41.817', $10 = '2026-01-27 10:02:41.596', $11 = '8948.05', $12 = '8951.7', $13 = '8943.4', $14 = '8949.85', $15 = '1483', $16 = '0', $17 = '2026-01-27 10:02:41.817', $18 = '2026-01-27 10:02:41.596'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:45:47 Duration: 0ms Database: postgres parameters: $1 = '2026-01-26 23:00:00', $2 = '256.12', $3 = '256.36', $4 = '254.965', $5 = '255.46', $6 = '2093', $7 = '515840249386608300', $8 = '0', $9 = '2026-01-27 10:45:47.245', $10 = '2026-01-27 10:45:47.16', $11 = '256.12', $12 = '256.36', $13 = '254.965', $14 = '255.46', $15 = '2093', $16 = '0', $17 = '2026-01-27 10:45:47.245', $18 = '2026-01-27 10:45:47.16'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-27 10:11:44 Duration: 0ms Database: postgres parameters: $1 = '2026-01-26 20:00:00', $2 = '17702.25', $3 = '17718.95', $4 = '17691.55', $5 = '17703.65', $6 = '1254', $7 = '515840248005928300', $8 = '0', $9 = '2026-01-27 10:11:44.838', $10 = '2026-01-27 10:11:44.689', $11 = '17702.25', $12 = '17718.95', $13 = '17691.55', $14 = '17703.65', $15 = '1254', $16 = '0', $17 = '2026-01-27 10:11:44.838', $18 = '2026-01-27 10:11:44.689'

14

102ms

1,577

Details

0ms

1ms

0ms

select category, ;

Times Reported Time consuming bind #14

Day	Hour	Count	Duration	Avg duration
	10	1,577	102ms	0ms

select category, ;

Date: 2026-01-27 10:00:40 Duration: 1ms Database: postgres parameters: $1 = '515852059296080307', $2 = 'symbol', $3 = 'AUDJPY', $4 = 'NZDJPY', $5 = 'USDMXN', $6 = 'EURMXN', $7 = 'USDJPY', $8 = 'USDNOK', $9 = 'CADJPY', $10 = 'NOKJPY', $11 = 'GBPJPY', $12 = 'ZARJPY', $13 = 'CHFJPY', $14 = 'USDZAR', $15 = 'HKDJPY', $16 = 'USDDKK', $17 = 'EURJPY', $18 = 'EURNOK', $19 = 'USDSEK', $20 = 'USDPLN', $21 = 'GBPZAR', $22 = 'EURSEK', $23 = 'EURTRY', $24 = 'USDCNH', $25 = 'GBPAUD', $26 = 'GBPNZD', $27 = 'ZARJPY', $28 = 'EURNZD', $29 = 'EURAUD', $30 = 'EURPLN', $31 = 'EURGBP', $32 = 'GBPCAD', $33 = 'USDZAR', $34 = 'EURNOK', $35 = 'EURCAD', $36 = 'USDCAD', $37 = 'EURSEK', $38 = 'NOKJPY', $39 = 'USDMXN', $40 = 'USDSEK', $41 = 'GBPUSD', $42 = 'EURCHF', $43 = 'EURMXN', $44 = 'CADJPY', $45 = 'CADCHF', $46 = 'EURNZD', $47 = 'USDSGD', $48 = 'GBPZAR', $49 = 'USDPLN', $50 = 'HKDJPY', $51 = 'EURPLN', $52 = 'GBPCAD', $53 = '515852059296080307', $54 = 'symbol', $55 = 'AUDJPY', $56 = 'NZDJPY', $57 = 'USDMXN', $58 = 'EURMXN', $59 = 'USDJPY', $60 = 'USDNOK', $61 = 'CADJPY', $62 = 'NOKJPY', $63 = 'GBPJPY', $64 = 'ZARJPY', $65 = 'CHFJPY', $66 = 'USDZAR', $67 = 'HKDJPY', $68 = 'USDDKK', $69 = 'EURJPY', $70 = 'EURNOK', $71 = 'USDSEK', $72 = 'USDPLN', $73 = 'GBPZAR', $74 = 'EURSEK', $75 = 'EURTRY', $76 = 'USDCNH', $77 = 'GBPAUD', $78 = 'GBPNZD', $79 = 'ZARJPY', $80 = 'EURNZD', $81 = 'EURAUD', $82 = 'EURPLN', $83 = 'EURGBP', $84 = 'GBPCAD', $85 = 'USDZAR', $86 = 'EURNOK', $87 = 'EURCAD', $88 = 'USDCAD', $89 = 'EURSEK', $90 = 'NOKJPY', $91 = 'USDMXN', $92 = 'USDSEK', $93 = 'GBPUSD', $94 = 'EURCHF', $95 = 'EURMXN', $96 = 'CADJPY', $97 = 'CADCHF', $98 = 'EURNZD', $99 = 'USDSGD', $100 = 'GBPZAR', $101 = 'USDPLN', $102 = 'HKDJPY', $103 = 'EURPLN', $104 = 'GBPCAD'
select category, ;

Date: 2026-01-27 10:06:06 Duration: 0ms Database: postgres parameters: $1 = '515852059319772307', $2 = 'symbol', $3 = 'BTCUSD', $4 = 'WTI', $5 = 'US30', $6 = 'NAS100', $7 = 'GER30', $8 = 'SPX500', $9 = 'CHI50', $10 = 'HK50', $11 = 'WTI', $12 = 'US30', $13 = 'SPX500', $14 = 'NAS100', $15 = 'BTCUSD', $16 = 'CHI50', $17 = 'GER30', $18 = 'HK50', $19 = '#TSLA', $20 = '#AAPL', $21 = '#AAPL', $22 = '#TSLA', $23 = '515852059319772307', $24 = 'symbol', $25 = 'BTCUSD', $26 = 'WTI', $27 = 'US30', $28 = 'NAS100', $29 = 'GER30', $30 = 'SPX500', $31 = 'CHI50', $32 = 'HK50', $33 = 'WTI', $34 = 'US30', $35 = 'SPX500', $36 = 'NAS100', $37 = 'BTCUSD', $38 = 'CHI50', $39 = 'GER30', $40 = 'HK50', $41 = '#TSLA', $42 = '#AAPL', $43 = '#AAPL', $44 = '#TSLA'
select category, ;

Date: 2026-01-27 10:01:55 Duration: 0ms Database: postgres parameters: $1 = '601729875362372307', $2 = 'symbol', $3 = 'JPN225', $4 = 'USA30', $5 = 'UK100', $6 = 'USA100', $7 = 'AUS200', $8 = 'USDIndex', $9 = 'NETH25', $10 = 'FRA40', $11 = 'SUI20', $12 = 'UK100', $13 = 'USDIndex', $14 = 'USA30', $15 = 'JPN225', $16 = 'USA100', $17 = 'AUS200', $18 = 'SPA35', $19 = 'SUI20', $20 = 'FRA40', $21 = 'SPA35', $22 = 'NETH25', $23 = '601729875362372307', $24 = 'symbol', $25 = 'JPN225', $26 = 'USA30', $27 = 'UK100', $28 = 'USA100', $29 = 'AUS200', $30 = 'USDIndex', $31 = 'NETH25', $32 = 'FRA40', $33 = 'SUI20', $34 = 'UK100', $35 = 'USDIndex', $36 = 'USA30', $37 = 'JPN225', $38 = 'USA100', $39 = 'AUS200', $40 = 'SPA35', $41 = 'SUI20', $42 = 'FRA40', $43 = 'SPA35', $44 = 'NETH25'

15

66ms

177

Details

0ms

SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Times Reported Time consuming bind #15

Day	Hour	Count	Duration	Avg duration
	10	177	66ms	0ms

SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Date: 2026-01-27 10:13:24 Duration: 0ms Database: postgres
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Date: 2026-01-27 10:13:25 Duration: 0ms Database: postgres
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Date: 2026-01-27 10:13:24 Duration: 0ms Database: postgres

16

60ms

13

Details

3ms

6ms

4ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #16

Day	Hour	Count	Duration	Avg duration
	10	13	60ms	4ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-01-27 10:01:02 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-01-27 10:13:15 Duration: 5ms Database: postgres parameters: $1 = '958', $2 = '958'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-01-27 10:16:36 Duration: 5ms Database: postgres parameters: $1 = '667', $2 = '667'

17

59ms

70

Details

0ms

1ms

0ms

SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Times Reported Time consuming bind #17

Day	Hour	Count	Duration	Avg duration
	10	70	59ms	0ms

SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-01-27 10:09:33 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'EURJPY', $3 = '558'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-01-27 10:15:35 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'BTCUSD', $3 = '558'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-01-27 10:46:14 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'JP225', $3 = '558'

18

51ms

1

Details

51ms

with maxwhid as ( ;

Times Reported Time consuming bind #18

Day	Hour	Count	Duration	Avg duration
	10	1	51ms	51ms

with maxwhid as ( ;

Date: 2026-01-27 10:11:16 Duration: 51ms Database: postgres parameters: $1 = '335', $2 = '621', $3 = '637', $4 = '642', $5 = '660', $6 = '666', $7 = '643', $8 = '630', $9 = '680', $10 = '641', $11 = '431', $12 = '622', $13 = '489', $14 = '529', $15 = '576', $16 = '665', $17 = '667', $18 = '558', $19 = '620', $20 = '125', $21 = '488', $22 = '567', $23 = '689', $24 = '700', $25 = '758', $26 = '763', $27 = '765', $28 = '817', $29 = '914', $30 = '972'

19

47ms

66

Details

0ms

select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Times Reported Time consuming bind #19

Day	Hour	Count	Duration	Avg duration
	10	66	47ms	0ms

select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-01-27 10:25:53 Duration: 0ms Database: postgres
select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-01-27 10:31:08 Duration: 0ms Database: postgres
select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-01-27 10:20:52 Duration: 0ms Database: postgres

20

47ms

83

Details

0ms

SELECT absolutetimezoneoffset;

Times Reported Time consuming bind #20

Day	Hour	Count	Duration	Avg duration
	10	83	47ms	0ms

SELECT absolutetimezoneoffset;

Date: 2026-01-27 10:01:01 Duration: 0ms Database: postgres parameters: $1 = '632', $2 = 'Commodities'
SELECT absolutetimezoneoffset;

Date: 2026-01-27 10:01:13 Duration: 0ms Database: postgres parameters: $1 = '632', $2 = 'Forex'
SELECT absolutetimezoneoffset;

Date: 2026-01-27 10:01:37 Duration: 0ms Database: postgres parameters: $1 = '632', $2 = 'Forex Majors'

Log levels

Key values

544,199 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	418	0.08%
DETAIL	120,286	22.10%
ERROR	34	0.01%
FATAL	2	0.00%
HINT	34	0.01%
LOG	422,831	77.70%
STATEMENT	594	0.11%

Events distribution

Key values

0 PANIC entries
2 FATAL entries
34 ERROR entries
0 WARNING entries

Most Frequent Errors/Events

Key values

34 Max number of times the same event was reported
36 Total events found

Rank

Times reported

Error

1

34

Details

ERROR: function fixcandlegaps(...) is not unique

Times Reported Most Frequent Error / Event #1

Day	Hour	Count
Jan 27	10	34

ERROR: function fixcandlegaps(unknown, boolean) is not unique at character 8

2

Details

FATAL: connection to client lost

Times Reported Most Frequent Error / Event #2

Day	Hour	Count
Jan 27	10	2

FATAL: connection to client lost

Global information

Overview

Global Stats

SQL Traffic

Key values

SELECT Traffic

Key values

INSERT/UPDATE/DELETE Traffic

Key values

Queries duration

Key values

Prepared queries ratio

Key values

General Activity

Connections

Established Connections

Key values

Connections per database

Key values

Connections per user

Key values

Connections per host

Key values

Sessions

Simultaneous sessions

Key values

Histogram of session times

Key values

Sessions per database

Key values

Sessions per user

Key values

Sessions per host

Key values

Sessions per application

Key values

Checkpoints / Restartpoints

Checkpoints Buffers

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Checkpoints Wal files

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Checkpoints distance

Key values

Checkpoints Activity

Checkpoint causes

Key values

Temporary Files

Size of temporary files

Key values

Number of temporary files

Key values

Temporary Files Activity

Queries generating the most temporary files (N)

Queries generating the largest temporary files

Vacuums

Vacuums / Analyzes Distribution

Key values

Analyzes per table

Key values

Vacuums per table

Key values

Tuples removed per table

Key values

Pages removed per table

Key values

Autovacuum Activity

Locks

Locks by types

Key values

Type of locks

Most frequent waiting queries (N)

Queries that waited the most

Queries

Queries by type

Key values

Queries by database

Key values

Queries by user

Key values

Duration by user